Bench Talk

Defining Agentic AI for Industrial Automation

Tue, 08 Sep 2026 22:01:41 GMT

How agentic AI can coordinate industrial workflows without replacing deterministic control

Source: WS Studio 1985/stock.adobe.com; generated with AI

Many industrial systems can identify potential problems before they disrupt production. For instance, a predictive maintenance model can detect abnormal vibration in a motor or pump. But the alert does not complete the maintenance task. Technicians still have to examine the asset, determine whether operating conditions explain the change, assess production requirements, review maintenance records, identify the necessary parts, and schedule the work.

These steps typically require people to move between processes, maintenance systems, production applications, and engineering documents. Industrial artificial intelligence (AI) can produce the initial insight, but turning that insight into a coordinated response remains a human task.

Agentic AI can help narrow this gap by coordinating the multistep work needed to move from insight to controlled action. In this first blog of our agentic AI series, we define agentic AI, examine the core technologies that enable it in industrial automation, and explain its potential importance to industrial operations.

What Is Agentic AI?

Agentic AI refers to a goal-oriented software system that can pursue an assigned objective through a continuous sequence of decisions and actions. The agent observes its operating context, interprets the objective, breaks it into smaller tasks, and determines what information or capabilities it needs.

It can then select authorized data sources, analytical models, and software tools, perform approved actions, and assess whether each step produced the expected result. This creates a workflow-level feedback cycle described as observe, interpret, plan, act, evaluate, and adapt.

For instance, consider an abnormal vibration detected by a predictive maintenance model. The agent can check the asset’s operating mode, retrieve alarms, review previous maintenance records, and determine if a work order already exists. It can then check the production schedule and the availability of spare parts before preparing a maintenance request. If the agent determines that the vibration occurred during startup or that a required part is unavailable, it can adjust the remaining workflow.

This behavior distinguishes agentic AI from other forms of automation and AI. Conventional automation follows logic and sequences. For example, a programmable logic controller repeatedly executes predefined control logic in response to process inputs. This deterministic behavior remains the appropriate choice for repeatable, time-critical machine and process control.

Agency also does not imply unrestricted autonomy in industrial operations. An industrial agent may have read-only access and provide recommendations to an engineer. It may assemble a proposed action and wait for approval, or it may execute a narrow set of preauthorized, low-consequence actions.

These limits are fundamental to an agentic AI ecosystem. The industrial agents should operate at the supervisory, analytical, or workflow coordination level. The permissions, approvals, interlocks, and established operating procedures will continue to define which agentic actions are permitted.

The appropriate objective is to maintain bounded agency to coordinate work more effectively without compromising existing controls and safety protections.

Core Technologies for Industrial Agentic AI

An industrial AI agent is an integrated architecture in which data infrastructure, analytical models, planning mechanisms, application interfaces, and operational controls are coordinated.

Each capability depends on the others. For instance, models need reliable context, plans require access to tools, tool calls must be governed, and the results of every action must be returned to the agent so it can determine the next step.

These requirements can be grouped into four interrelated capabilities: contextualized industrial data, analysis and reasoning, planning and orchestration, and governed access to industrial tools.

Contextualized Industrial Data

An agent needs an accurate representation of the environment in which it operates. Depending on the task, this representation may include sensor measurements, equipment states, operating modes, alarms, maintenance histories, production schedules, spare parts information, operating manuals, and standard procedures.

However, access to data alone does not provide the complete operational context. Open Platform Communications Unified Architecture (OPC UA) is relevant because it provides mechanisms for representing industrial information as structured objects, variables, types, relationships, events, and methods.

Earlier this year, the OPC Foundation announced plans to convert more than 430 Companion Specifications into formats optimized for retrieval-augmented generation (RAG), the Model Context Protocol (MCP), and AI-assisted engineering workflows.^[1]

Analysis and Reasoning

Once the agent has relevant context, it needs computational capabilities to interpret the information and support decision-making. These capabilities can include machine learning models for anomaly detection and forecasting, computer vision models for inspection, and optimization algorithms for scheduling.

For instance, a language model can help by interpreting a high-level objective, retrieving information from manuals, summarizing evidence, or determining which available tool could perform the next task. However, a validated vibration model remains better suited for detecting abnormal vibration.

The agent coordinates between these components. It may call a predictive model to analyze equipment condition, consult a large language model to interpret maintenance documentation, and apply deterministic rules to verify that the proposed plan meets operating requirements.

Planning and Orchestration

Planning turns the objective into individual tasks and sets the order in which they should be completed. As the work progresses, the agent can identify missing information, select the required analytical tools, and pause at points where an engineer must approve the next step.

The plan is not necessarily fixed. If plant conditions change while approval is pending, the original recommendation may no longer be valid. The agent can check the latest operating state, revise the plan, and submit the updated plan for review.

Orchestration manages this process from one step to the next. It maintains fixed controls for tasks that require predictable behavior while allowing the workflow to adapt as new evidence emerges. If one agent can handle the task with restricted permissions and a limited set of tools, adding more agents may introduce unnecessary coordination overhead.

Governed Access to Tools

To execute a workflow, the agent needs interfaces to industrial and business applications. It might read equipment status from a supervisory control and data acquisition (SCADA) platform, retrieve production information from a manufacturing execution system (MES), or check parts availability in an enterprise resource planning (ERP) system. Scheduling software could then indicate when the proposed work can be performed.

The agent selects the application required for the current task, sends the necessary information, and verifies whether the query succeeded. Once AI is connected to operational software, a reasoning error can have consequences. Selecting the wrong asset could disrupt an industrial workflow.

Governance must therefore be integrated into the architecture.

Why Agentic AI Matters to Industrial Automation

The case for agentic AI does not begin with a lack of automation or analytical intelligence.

Industrial facilities may already use combinations of sensors, controllers, alarms, machine learning models, computer vision systems, optimization software, and digital twins. The challenge is that these capabilities are specialized, distributed across multiple systems, and disconnected from the end-to-end operational workflow.

Agentic AI does not need to replace these tools or generate a superior answer on its own. Instead, it can determine when each tool is relevant, provide the required inputs, route the output to the next step, and maintain the state of the overall workflow.

Agentic AI offers a different form of adaptability. Because the agent evaluates the outcome of each step, it can revise the remaining workflow when new information changes the situation. It may query another source, select a different analytical tool, request additional approval, or escalate the issue to a person.

Agents are better suited to supervisory decisions and cross-system workflows than for real-time or safety-critical control. The industrial agent may observe information from controllers, prepare a requested change, or coordinate an approved workflow around the process.

Siemens describes an industrial AI agent architecture in which an orchestrator selects specialized agents for tasks across its Industrial Copilot ecosystem. The company positions these capabilities across design, planning, engineering, operations, and service and states that users choose which tasks to delegate.

Conclusion

Agentic AI extends industrial intelligence by coordinating the steps required to respond to an operational problem. The strongest industrial use case will be multistep, context-dependent processes in which insights already exist, but actions are distributed across industrial systems.

Bounded industrial agentic AI will therefore reduce the gap between identifying a problem and implementing a coordinated and controlled response.

The next blog in this series will examine how AI agents can enhance predictive maintenance by integrating data, workflows, and decision-making across industrial systems.

[1] https://opcfoundation.org/news/press-releases/opc-foundation-advances-opc-ua-for-the-ai-era-with-companion-specifications-optimized-for-agentic-ai/

How MQTT Simplifies Industrial Connectivity

Thu, 20 Aug 2026 20:45:55 GMT

MQTT reduces point-to-point integration and supports scalable data exchange across brownfield industrial systems

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Effective industrial connectivity is important for moving machine and process data between sensors, programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA) systems, and enterprise applications. Implementing the appropriate communication protocols directly supports condition monitoring, predictive maintenance, energy management, and production analysis.

However, connectivity does not scale easily in brownfield facilities. These plants have several generations of PLCs, sensors, fieldbuses, and proprietary systems that were designed for local control. As such, they struggle to share industrial data with enterprise and cloud applications.

The Message Queuing Telemetry Transport (MQTT) protocol addresses this by providing a lightweight publish/subscribe (pub/sub) messaging layer for industrial data. The protocol allows the edge gateway to publish data once to an MQTT broker and then distribute it to multiple authorized systems.

In our last part of this blog series on industrial networking, we explored the ways Single Pair Ethernet (SPE) frees automation systems of several constraints associated with traditional Ethernet. Here, we dig into why conventional industrial connectivity becomes difficult to scale, how MQTT reduces point-to-point integration through a broker-based pub/sub architecture, and how MQTT 5.0 and Sparkplug support manageable industrial deployments.

Why Industrial Connectivity Struggles at Scale

While industrial connectivity operates effectively at a small scale, it becomes harder to manage as more devices, protocols, applications, and sites are added.

Legacy Systems and Multiple Protocols

The wide range of fieldbus and industrial protocols makes it difficult and time-consuming to integrate data from sensors, PLCs, and remote devices. It also makes standardization harder.

Industrial protocols, such as Modbus Transmission Control Protocol (TCP), Ethernet Internet Protocol (EtherNet/IP), and Open Platform Communications Data Access (OPC DA), rely heavily on a synchronous, request-response, client-server model. In this model, an external client (e.g., a SCADA system) must continuously request data from the server (e.g., the PLC) at a fixed interval (e.g., every 500 milliseconds), even if the process value has not changed.

Continuous polling has several problems, including high network overhead and processor saturation. Managing multiple connection requests and executing continuous read/write cycles consumes central processing unit (CPU) resources. This can degrade the performance of the controller’s real-time safety and execution loops.

OPC Unified Architecture (UA) addressed many of these issues by introducing platform independence, robust security profiles, and an object-oriented, browsable address space. However, OPC UA client-server architectures still require a direct connection between every client and the server.

Point-to-Point Integration Complexity

Point-to-point integration becomes harder to manage as more devices and applications are added to the network. In a direct point-to-point architecture, connecting 50 PLCs to five enterprise applications could require as many as 250 separate application-to-PLC connections. Adding to the complexity is that all applications will have to maintain their own link to every PLC.

Even a change to the tag name, register address, or network configuration would require updates in every connected application. If the engineering team does not update one connection, it can disrupt the data flow and make the overall architecture difficult to maintain.

Scaling from PoC to Production

At the proof-of-concept (PoC) stage, custom Python scripts, local CSV file exports, or SQL database connections work for a single-connection pilot. But production-grade deployment introduces operational requirements that these custom, ad hoc integrations cannot fulfill.

In the case of remote sites, the network connectivity depends on high-latency, low-bandwidth, or metered cellular and satellite networks. The continuous, high-frequency polling over these connections can be cost-inefficient and unreliable.

In addition, operational governance is crucial. When the systems are scaled to thousands of data points, they require standard naming rules, consistent data schemas, and centralized access controls. Without these, the broker can accumulate fragmented and unorganized data.

Designing Industrial Data Connectivity for Scalability

To address the limitations of traditional industrial communications, operational technology (OT) architects are adopting MQTT, which typically runs over TCP/IP or another transport that provides ordered, lossless, and bidirectional communication.

Unlike direct client-server integrations, MQTT uses a broker to separate data producers from data consumers. Publishers send messages to named topics on the broker, while subscribers receive messages from the topics they need. In this approach, the publisher does not need to know which applications are consuming the data, and subscribers do not need direct connections to the original device.

MQTT does not necessarily eliminate polling at the machine level. In a brownfield facility, an edge gateway may still poll a PLC through Modbus TCP, OPC DA, or another legacy protocol. The difference is that the gateway collects the value once and publishes it to the MQTT broker, which then distributes the message to every authorized subscriber. This reduces the number of direct PLC connections and prevents multiple applications from acquiring the same data independently.

For example, an edge gateway can read vibration data from a motor drive over Modbus TCP and publish it to an MQTT topic. The same data can then be used by a SCADA system, a predictive maintenance tool, an energy management application, and a cloud data lake.

This solution allows the gateway to publish once and have four consumers receive the data without any of them ever connecting to the drive or the Modbus network. Adding a fifth consumer requires no change to the gateway or the drive; the consumer only has to subscribe to the relevant MQTT topic on the broker.

MQTT Standards for Industrial Deployments

It is important to understand the different MQTT standards for successfully implementing networking technology in complex industrial environments.

MQTT 3.1.1

MQTT 3.1.1 has been widely adopted across Internet of Things (IoT) and telemetry systems because of its simplicity, small code footprint, and low packet overhead. It supports a minimum two-byte fixed header, three quality-of-service (QoS) levels, persistent sessions, and retained messages.

However, MQTT 3.1.1 provides limited operational context for large industrial deployments. It does not include standardized packet-level fields for adding custom metadata, although applications can still place metadata inside the message payload. Its

Additionally, error reporting with MQTT 3.1.1 is basic. CONNACK packets can identify a small number of connection failures, such as invalid credentials or insufficient authorization, while a rejected subscription is reported only as a general failure. This limited feedback makes it harder to identify and troubleshoot problems across remote systems.

MQTT 5.0

MQTT 5.0 extends the broker-based pub/sub model of MQTT 3.1.1, with features that make large deployments easier to operate. These additions improve error reporting, connection management, metadata handling, and message distribution across multiple applications.

One of the key changes is the use of reason codes. With MQTT 5.0, packets such as CONNACK, PUBACK, SUBACK, and DISCONNECT can include a standardized code that explains if an operation succeeded or why it failed. This gives clients and operators information when troubleshooting connections.

The standard also allows the broker to send a DISCONNECT packet before closing a connection. The packet can include a machine-readable reason code and, optionally, a human-readable reason string. For example, a broker approaching its connection limit could tell a client that the server is unavailable, that the client has exceeded a quota, or that the client should reconnect to another server.

Sparkplug

Even with MQTT’s efficient pub/sub transport, it does not define how industrial topics should be organized, how process values should be encoded, or how applications should determine whether a device and its data remain valid.

Different vendors may use their own topic structures and payload formats, making it difficult for SCADA and analytics applications to interpret the data consistently. Sparkplug addresses this problem by adding a common industrial topic structure, payload format, and device-state model on top of MQTT.

Sparkplug is an open specification hosted by the Eclipse Foundation that defines an MQTT topic namespace, a payload representation, and a session-state management model for industrial applications. The current Sparkplug 3.0 establishes rules for how MQTT clients representing industrial edge nodes, devices, and host applications should organize and exchange information consistently.

Conclusion

As industrial organizations connect more assets, applications, and remote sites, traditional point-to-point integrations become increasingly difficult to maintain. MQTT addresses this challenge with a lightweight publish/subscribe architecture that decouples data producers from data consumers, reducing connection complexity and improving scalability.

By enabling edge gateways to collect data once and distribute it through a broker, MQTT helps organizations extend the value of existing brownfield equipment without overloading controllers or creating hundreds of direct integrations. MQTT 5.0 further improves large-scale deployments with enhanced diagnostics and connection management, while Sparkplug adds the standardized topic structure, payload format, and device-state awareness needed for reliable industrial interoperability.

Together, these technologies provide a practical foundation for moving operational data from the plant floor to SCADA, analytics, cloud, and enterprise systems, helping manufacturers build more connected, manageable, and data-driven operations.

Understanding Single Pair Ethernet for Industrial Automation

Fri, 31 Jul 2026 14:14:04 GMT

SPE reduces cabling while extending Ethernet connectivity, bandwidth, and power delivery to industrial field devices

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Ethernet has been one of the most important communication standards in industrial automation for connecting controllers, machine vision systems, control cabinets, supervisory platforms, and enterprise networks. It has standardized interfaces, high data rates, protocol interoperability, and a mature ecosystem for increasingly data-intensive automation architectures.

But standard multi-pair Ethernet does not scale efficiently to industrial field devices such as sensors, actuators, and distributed input/output (I/O) modules. Moreover, the deployment of four-pair cable assemblies can become too large, costly, and mechanically inflexible (Figure 1). Cable volume and bend-radius constraints also become significant when hundreds or thousands of field devices are installed.

Figure 1: Typical Ethernet cables use two or four twisted pairs of wires. (Source: aleksandrbs/stock.adobe.com)

Previously, we examined how Time-Sensitive Networking (TSN) enables predictable, low-latency communication across converged industrial Ethernet networks. In this blog, we dig into how Single Pair Ethernet (SPE) aims to address the constraints of traditional Ethernet by providing the same capabilities without the size, wiring complexity, and installation cost of conventional multi-pair physical media. SPE uses a single balanced pair of copper wires and offers optional power delivery for constrained industrial networks.

What Is Single Pair Ethernet?

SPE maintains the standard Ethernet frame format and the higher layers of the Open Systems Interconnection (OSI) model while simplifying the physical medium. It uses advanced modulation and echo cancellation to achieve full-duplex communication on two wires. This communication protocol provides several advantages for industrial engineers:

Reduction of passive components: SPE reduces the wire count from eight to two for thinner, lighter, and more flexible cables.
Simplified PCB design: SPE transceivers, or physical layer (PHY) devices, require fewer pins and smaller magnetics than multi-pair PHY devices, enabling compact device designs.
Cable reuse potential: For some 10BASE-T1L retrofit applications, SPE can be implemented over existing twisted-pair fieldbus cabling (such as Type A fieldbus cable) if the cable meets basic impedance and attenuation requirements.

SPE Standards

The successful deployment of SPE depends on comprehensive standards. The IEEE 802.3 committee has developed a range of SPE physical layer specifications, each designed to meet specific industrial automation requirements.

These standards define data rates, transmission distances, signaling schemes, and network topologies. A key SPE amendment for industrial automation is IEEE 802.3cg-2019, which defines two different 10Mb/s PHYs: 10BASE-T1L and 10BASE-T1S.

10BASE-T1L Ethernet

10BASE-T1L is designed for long-range, point-to-point communication that can transmit full-duplex 10Mb/s Ethernet data over distances of up to 1,000m. This capability is important in process automation industries where facilities require long cable runs between field instrumentation and control rooms.

This standard provides an Ethernet-based migration path for installations that currently use legacy 4mA to 20mA analog loops and fieldbuses such as PROFIBUS PA and Foundation Fieldbus. This eliminates the requirement for protocol gateways and enables direct IP connectivity to industrial field devices.

10BASE-T1L uses a three-level pulse amplitude modulation (PAM3) scheme at a 7.5 megabaud (MBd) symbol rate. It has two transmit amplitude modes: a standard 1.0V peak-to-peak (Vpp) for most links and an optional 2.4Vpp level that can be auto-negotiated to overcome signal attenuation.

10BASE-T1S Ethernet

10BASE-T1S is optimized for short-range, multidrop applications. It uses a bus topology in which eight nodes share a single cable segment up to 25m long. This architecture is ideal for high-density applications, such as machine control cabinets and distributed I/O systems.

Even though 10Mb/s is sufficient for most field devices, higher bandwidth is sometimes required for machine vision and advanced sensor fusion applications. IEEE 802.3bw (100BASE-T1) provides 100Mb/s full-duplex communication over a single twisted pair with a reach of up to 15m on an unshielded cable.

For applications requiring gigabit bandwidth, IEEE 802.3bp defines 1000BASE-T1 to provide full-duplex 1Gb/s communication. This standard has an optional Type B link segment that supports at least 40m of reach and up to four inline connectors that extend SPE reach to industrial automation networks.

At the highest end of the performance spectrum is IEEE 802.3ch, which defines multi-gigabit SPE for 2.5Gb/s, 5Gb/s, and 10Gb/s, but its 15m reach limits its industrial application to short-distance, high-bandwidth scenarios. An example application includes connecting a vision sensor to its processor on a robotic arm.

Enhancing SPE with PoDL

One optional capability of SPE is to deliver electrical power simultaneously with data over the same two wires. Power over Data Lines (PoDL) is the technology enabling such an approach, and it was standardized in IEEE 802.3bu-2016 and further expanded in subsequent amendments of IEEE 802.3cg. Similarly, Single Pair Power over Ethernet (SPoE) delivers power and data over the same single balanced pair used for SPE transmission.

PoDL is the functional equivalent of Power over Ethernet (PoE) for the two-wire connection. The operation of PoDL is controlled by a complex, safety-oriented protocol that manages the interaction between the power sourcing equipment (PSE) supplying power (e.g., an SPE switch) and the power delivery (PD) device consuming the power (e.g., a field sensor).

Before supplying substantial power to the line, PSE performs a detection sequence to verify that a PoDL-compliant PD is connected to the network. This prevents the PSE from supplying power to a device that is not designed to receive it. After successful detection, the PSE proceeds to an optional classification phase using the Serial Communication Classification Protocol (SCCP).

This is a low-speed, bidirectional communication process that allows the PD to communicate with the PSE for its specific power requirements. The PSE then allocates and delivers the necessary voltage and current to optimize power management across the industrial network. Overall, the entire process ensures safe startup, continuous operation, and protection against fault conditions like short circuits.

Conclusion

SPE offers a compelling alternative to traditional fieldbus and multi-pair Ethernet systems. Combining reduced wiring, flexible installation options, standardized communication, and integrated power delivery, SPE helps streamline the transition toward more connected and data-rich industrial environments. Through standards such as 10BASE-T1L and 10BASE-T1S, SPE supports a wide range of industrial applications, from long-distance process instrumentation to short-reach multidrop networks. When paired with PoDL, it can also deliver power and data over the same cable, reducing infrastructure complexity while enabling direct connectivity to field devices.

In the last installation of this industrial networking blog series, we turn our attention to Message Queuing Telemetry Transport (MQTT) and the ways it is advancing Industrial Internet of Things (IIoT) communications.

How TSN Brings Deterministic Timing to Industrial Ethernet

Wed, 29 Jul 2026 05:01:00 GMT

Time-Sensitive Networking enables predictable, low-latency communication across converged industrial Ethernet networks.

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Modern industrial automation is a hyper-connected environment in which synchronized multi-axis motion control and collaborative robotics must operate within a shared networked architecture where timing is as critical as throughput. For example, in coordinated multi-robot welding, even a few milliseconds’ delay in a control signal to one robot can disrupt synchronization, trigger a fault condition, or reduce process accuracy.

In high-stakes manufacturing and motion control applications, time synchronization among robots, sensors, controllers, and actuators is vital. Any unpredictable delay can lead to misalignment and production errors. Deterministic communication in such systems ensures that every component operates within a predictable window and executes its programmed tasks with the required accuracy.

Ethernet has become the common networking platform for industrial automation, but traditional Ethernet was never designed to provide bounded delivery times for critical data. As factories increasingly combine motion control, robotics, sensors, machine vision, and information technology (IT) systems on shared infrastructure, predictable communication timing has become essential. Time-Sensitive Networking (TSN) addresses this challenge by adding deterministic capabilities to standard Ethernet.

In this first blog of our series on industrial Ethernet, we explore how TSN uses time synchronization, traffic scheduling, frame preemption, and reliability mechanisms to deliver the bounded latency and low jitter required by modern industrial applications.

What Is Time-Sensitive Networking?

TSN is a suite of IEEE 802.1 standards that extends traditional Ethernet with tools for deterministic performance.^[1] TSN aims to provide bounded latency, low packet delay variation (i.e., jitter), and improved reliability for selected traffic by reserving resources for critical traffic and applying advanced queuing and shaping techniques.

TSN allows the same network to carry real-time motion control traffic, video streams, and standard IT traffic simultaneously, provided the network is engineered so that each traffic class receives the required Quality of Service (QoS). TSN operates at Layer 2 (Data Link Layer) of the Open Systems Interconnection (OSI) model, which makes it the foundation on which higher-layer protocols can build.

TSN Standards: Time, Scheduling, Preemption, and Reliability

The fundamental requirement for deterministic operation within TSN is a shared understanding of time among all network participants. Without a common time base, the network switches cannot coordinate transmission windows and end stations cannot align time-sensitive communication. The task of establishing and maintaining this network-wide time synchronization is specified in the IEEE 802.1AS standard, also known as the generalized Precision Time Protocol (gPTP).

IEEE 802.1AS provides Layer 2 timing and synchronization and can support sub-microsecond clock alignment in properly designed TSN networks.^[2] In a TSN time-aware domain, network bridges and end stations synchronize their local clocks to a shared Grandmaster (GM) clock. The protocol operates via logical syntonization, in which the ratio between the local clock and the GM clock is calculated to offset for frequency drift.

After IEEE 802.1AS synchronizes the clocks of all participating devices to create a common network-wide clock, TSN can implement traffic scheduling. Among the TSN traffic scheduling mechanisms, the most critical for hard real-time industrial control is the IEEE 802.1Qbv Time-Aware Shaper (TAS).^[3] The TAS applies a Time-Division Multiple Access (TDMA) concept to Ethernet switches by dividing transmission time into repeated scheduled cycles. Each egress port on a TSN switch is equipped with transmission gates for each of its priority queues. A Gate Control List (GCL) defines a schedule of when these gates are open or closed.

For example, a 1ms cycle can be divided into separate time windows. In the first window, only the gate for high-priority scheduled traffic is open, allowing time-critical motion control packets to be transmitted. In the second window, the gates for best-effort traffic open, allowing regular IT or diagnostic data to pass. This protected time window prevents motion control packets from being delayed by lower-priority traffic.

While the TAS protects scheduled traffic from other data streams, problems can still occur. A low-priority Ethernet frame might begin transmission just before a high-priority critical frame arrives at the egress queue. Even if the TAS gate for the critical frame is open, it must wait for the large frame’s transmission to be completed, introducing significant blocking delay. For a 1Gbps link, transmitting a full-size frame takes over 12μs, a delay that may be unacceptable for some control applications.

To address this, TSN uses frame preemption, defined by IEEE 802.1Qbu (Frame Preemption) and IEEE 802.3br (Interspersing Express Traffic).^[4] The two standards allow a high-priority frame to interrupt a lower-priority, preemptable frame mid-transmission. The interrupted frame is fragmented, the priority frame is sent immediately, and the remaining fragments are transmitted once the priority frame is finished.

Other applicable standards include:

IEEE 802.1CB (Frame Replication and Elimination for Reliability) improves resilience by replicating frames over redundant paths and eliminating duplicates at the receiver.^[5]
IEEE 802.1Qci (Per-Stream Filtering and Policing) helps prevent a misbehaving or misconfigured stream from monopolizing network resources or degrading other traffic.^[6]
IEEE 802.1Qcc (Stream Reservation Protocol) provides models for network configuration and resource management.^[7]

How Industrial Protocols Use TSN

The bounded latency of TSN as a unified, deterministic Layer 2 Ethernet framework has led industrial automation standards organizations to adapt their existing protocols for TSN-enabled networks. This approach allows these organizations to retain the features, diagnostics, and object models of their protocols while using TSN to achieve deterministic Ethernet behavior at the network layer.

One of the most important examples of this shift is the integration of Open Platform Communications Unified Architecture (OPC UA) over TSN. This integration supports vendor-neutral, interoperable communication across controllers, devices, and higher-level systems. In this approach, OPC UA uses the publish/subscribe (Pub/Sub) model rather than the traditional client/server model. Pub/Sub is better suited for control and automation traffic because a single publisher can efficiently distribute data to multiple subscribers. The messages are encoded using the UA Datagram Protocol (UADP) and sent over User Datagram Protocol/Internet Protocol (UDP/IP).

TSN then provides the underlying Ethernet timing and traffic control operations. IEEE 802.1AS provides a shared time base across time-aware devices, while IEEE 802.1Qbv schedules when specific traffic classes can transmit. In this way, OPC UA defines the information model and communication semantics, while TSN helps provide predictable transport behavior for time-sensitive industrial communication.

Other industrial Ethernet ecosystems, including PROFINET, EtherNet/IP using the Common Industrial Protocol (CIP), and CC-Link IE TSN, have also adapted their protocols to TSN-enabled Ethernet.

Conclusion

Time-Sensitive Networking is a set of deterministic Ethernet capabilities that allows an industrial network to be designed with more predictable timing and better coexistence between critical and non-critical traffic. For industrial automation, that matters because factory communication is about servo coordination, distributed sensing, vision, edge analytics, and the convergence of operational technology (OT) and IT on shared infrastructure.

The future of industrial automation will continue to depend on how well the network can handle mixed traffic. Now that we’ve examined how TSN factors into that future, we will next explore the role of Single Pair Ethernet (SPE) in enabling efficient communication in automation systems.

Sources

[1] https://1.ieee802.org/tsn/
[2] http://standards.ieee.org/ieee/802.1AS/11968/
[3] http://standards.ieee.org/ieee/802.1Qbv/6068/
[4] http://standards.ieee.org/ieee/802.1Qbu/5464/; https://standards.ieee.org/ieee/802.3br/5814/
[5] https://standards.ieee.org/ieee/802.1CB/5703/
[6] https://standards.ieee.org/ieee/802.1Qci/6159/
[7] https://standards.ieee.org/ieee/802.1Qcc/5784/

How vPLCs Are Changing Industrial Control Systems

Thu, 04 Jun 2026 20:04:21 GMT

Breaking the Cycle of Proprietary Vendor Lock-In

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Most programmable logic controllers (PLCs) in industrial automation are proprietary, with tightly coupled hardware and software and a vendor-specific ecosystem. The proprietary nature of PLCs creates challenges for data integration, leading to siloed systems that conflict with the modern need for flexible, interoperable manufacturing systems. These constraints result in operational bottlenecks that hinder the implementation of modular production and plant-wide optimization strategies.

The industry is responding to these constraints by moving toward software-defined automation (SDA), in which the automation logic—including control, motion, safety, and visualization functions—is no longer tied to specific proprietary hardware. Instead, it runs as a portable software workload in a virtual machine or a container. The core principle of SDA is to treat the control logic as code.

The most practical implementation of this SDA approach is the virtual PLC (vPLC). This software-based control runtime executes in a virtualized environment on commercial-off-the-shelf (COTS) computing hardware. Whereas before we examined the PLC selection criteria designers consider when developing automation control systems, this final blog of our PLC series zeroes in on the advantages of choosing the virtual route. Overall, the shift toward virtualized environments aims to eliminate vendor lock-in, enabling scalability and facilitating the easy integration of logic control systems with an enterprise-level data platform.

Understanding the vPLC Runtime

At the core of a vPLC is its runtime engine. This software application is responsible for parsing and executing control logic programs written in languages compliant with the IEC 61131-3 standard. Adhering to this standard improves portability at the language level. However, practical portability across vendors still depends on exchange formats, libraries, toolchains, and runtime-specific features.

One of the key characteristics of conventional PLCs is deterministic, cyclic behavior. The vPLC runtime relies heavily on the scheduling, interrupt handling, and memory management capabilities of its host operating system (OS) to replicate this. For deterministic control, a vPLC relies on a real-time-capable host environment, such as a real-time operating system (RTOS) or Linux OS with real-time extensions and tuning. The priority-based preemptive scheduler within the RTOS is designed to ensure that high-priority, time-critical tasks, such as the vPLC’s control loop, are executed precisely within their specific deadlines.

Managing memory is also important for reducing latency. Converting memory addresses between the guest OS running the vPLC and the physical hardware introduces potential overhead. To solve this, vPLC host systems use hardware-assisted virtualization features built into modern central processing units (CPUs), such as Intel^® Extended Page Tables (EPT) or AMD Rapid Virtualization Indexing (RVI).

While the execution model of the vPLC is designed to mimic the predictable behavior of a traditional PLC’s scan cycle, it does so through a different mechanism. vPLCs realize cyclic behavior through software scheduling. The control runtime executes as a high-priority, periodic task managed by a real-time capable host operating system. This scheduler is responsible for invoking the control task at defined intervals to maintain deterministic execution.

At each scheduled execution, the vPLC performs a single logic scan; reads current process input/output (I/O) from a fieldbus, networked I/O, or other interfaces; executes the control logic; and writes outputs back through those interfaces. After completing its cycle, the task goes back to sleep until the scheduler wakes it for the next interval.

Deployment Architectures for SDA

The practical implementation of SDA depends on the choice of virtualization technology and the strategy for deploying workloads. Two common approaches for deployment utilize hypervisor stacks and containerization.

Hypervisor-Based Deployment

Hypervisor-based deployment is a mature technology that involves a software layer (i.e., the hypervisor) creating and managing one or more completely separate virtual machines (VMs) on a single physical server. Industrial applications commonly use server-grade hypervisor stacks, such as VMware ESXi or kernel-based virtual machine (KVM) platforms configured for real-time operations. These stacks run directly on the server’s hardware, reducing performance overhead compared to Type 2 hypervisors that run on top of a host operating system.

The architectural benefit of the hypervisor model is its strong isolation. The hypervisor abstracts the physical hardware and provides a virtualized set of hardware to each guest VM. Each VM then runs its own independent guest operating system. This allows multiple, disparate operating systems to run securely and concurrently on the same physical machine.

Container-Based Deployment

Containerization is a more lightweight form of OS-level virtualization. Instead of virtualizing the entire hardware stack, containers run as isolated user-space processes that share the single operating system kernel of their host machine. A container engine uses features within the Linux kernel to create the sandboxed container environment.

The advantage of this approach is efficiency. Because container images do not require a guest OS, they are much smaller than VM images. Therefore, their memory consumption is lower, and they can be started almost instantaneously. This allows a much higher workload density to be deployed on a given piece of hardware and enables rapid, agile deployment of workloads in line with modern DevOps practices.

This software-based deployment model also makes automation logic easier to manage by using common software development practices. When control applications are deployed as software workloads, engineering teams can store logic in version-control systems, test changes in virtualized environments, and promote validated updates across development and production systems.

While industrial control still requires strict validation, safety checks, and deterministic performance, this approach enables the application of Continuous Integration and Continuous Deployment (CI/CD)-inspired workflows to automation systems.

Conclusion

The shift to software-defined automation is setting the stage for a future where factory floors are highly scalable and manageable. The most significant long-term impact of this approach will be the adoption of DevOps methodologies in the industrial automation domain. By treating control logic as version-controlled software, manufacturers can adopt workflows rooted in CI/CD for testing, tracking, and deploying validated changes more consistently.

Finally, as control runtimes become increasingly standardized, software packages compliant with open standards such as IEC 61499 and running on COTS hardware will bring the vision of vendor independence closer to reality. Achieving this vision could finally break the cycle of proprietary vendor lock-in that has constrained the industry for decades.

Selecting a PLC Platform for Modern Industrial Automation

Wed, 03 Jun 2026 22:58:30 GMT

(Source: Mia/stock.adobe.com)

Selecting a programmable logic controller (PLC) platform is one of the most consequential decisions in the life cycle of an industrial automation system. Engineers often base this decision on immediate capital expenditure constraints and legacy vendor relationships. The repercussions of this choice can extend beyond decades of operational expenses, maintenance issues, and scalability bottlenecks.

Today, control system engineers face a paradox of specifying hardware for a production environment that will continue to change over several years. The industrial controller selected for a greenfield facility must operate reliably in the plant-floor hardware environment and remain relevant in an era of rapid convergence of information technology (IT) and operational technology (OT).

In our previous blog in this PLC series, we laid out the constraints that guide engineers in evaluating different industrial controller architectures. Here, we outline an engineering-driven decision framework for selecting a PLC platform that meets a project’s specific control requirements, integration needs, and long-term constraints.

Control Requirements

The fundamental requirement of any industrial controller is determinism. This ensures that the control system responds to a specific set of inputs with a corresponding set of outputs within a defined, predictable time window. Designers must consider how the PLC’s runtime schedules tasks and synchronizes input/output (I/O) updates. In practice, PLCs may use a main scan task (e.g., continuous scan) plus periodic and/or event-driven tasks, so it’s important to understand the execution model and timing guarantees.

The scan task is a repeating loop that reads inputs, executes logic, and writes outputs at a cycle time determined by instruction execution time and system overhead. For instance, if the code contains conditional jumps that change with process state, scan time will vary. Even when a continuous task interacts with a high-frequency input without synchronization, aliasing occurs. To achieve tighter determinism, high-performance architectures rely on time-driven or periodic task execution.

In periodic execution, the real-time operating system (RTOS) uses a preemptive scheduler to interrupt lower-priority tasks and execute critical control loops at a fixed interval. However, the risk in periodic architectures is overrun. For example, if a task is scheduled every 10ms but inefficient programming or memory latency results in a worst-case execution time of 11ms, the watchdog timer may trip. This requires the engineer to budget execution time and leave headroom to account for worst-case execution paths.

For applications that demand an immediate reaction to physical input, such as detecting a product on a conveyor, the PLC must support low-latency hardware interrupts. To meet this requirement, event tasks are triggered by specific hardware events rather than by clock cycles. This approach offloads detection from the main scan and allows the central processing unit (CPU) to interrupt execution immediately to run the corresponding reaction logic.

Programming Environment

Engineering workflow efficiency in new industrial automation infrastructure depends on how developers write, simulate, debug, and maintain code. These factors have become some of the most significant variables in the total cost of ownership.

Modern PLCs can be programmed using the IEC 61131-3 standard languages, which include Ladder Diagram (LD), Function Block Diagram (FBD), and Structured Text (ST). LD, also known as ladder logic, is popular for discrete logic because it is easy to understand, while ST is a high-level, code-like language used for array handling and complex logic. FBDs are ideal for process controls and are often used in safety logic configurations.

Engineers must verify which languages are available on the specific PLC under consideration and confirm if there are any limitations. For example, a smaller micro-PLC might support only LD or a vendor-specific functional block language, limiting the complexity of the algorithms implemented. Not every PLC supports all languages, but having at least LD, ST, and FBD provides sufficient flexibility.

There are times when the PLC’s integrated development environment (IDE) becomes more important than the raw CPU specifications. Ideal PLC development software should provide online monitoring, stepping, breakpoints, and simulation capabilities. Because these factors can affect development time and troubleshooting, engineers must consider how easy it is to debug the PLC in the field.

Scalability Constraints

When defining CPU and memory requirements, engineers must not only meet but exceed the immediate requirements within reasonable bounds. The aim should be to run the PLC at a moderate load to provide sufficient headroom for transient spikes and future additions. If the PLC is near 100 percent usage at startup, even small changes could lead to scan overruns.

As systems evolve with tighter timing requirements and additional sensors, diagnostics, and communications, the PLC may not be able to handle the load. Under-specifying the CPU is a common long-term mistake engineers make during development. For example, older PLCs may have been sufficient for basic discrete logic, but when engineers try to add proportional-integral-derivative (PID) control or data logging, the CPU cycle time increases significantly.

Memory constraint is another important aspect affecting scalability. Engineers must choose a memory size that is much larger than the initial requirement. Most control systems have a 10- to 20-year life cycle, and engineers often want to add improvements over time. Therefore, it is much easier to implement such additions if the PLC has spare performance capacity.

Engineers must also determine whether the PLC can be upgraded to a higher-performance CPU without requiring a full system replacement, and whether the controller supports multithreading. The goal is to understand how the PLC handles multiple tasks and distributed processing to enable the addition of advanced sensors or an artificial intelligence (AI) module. These additions become increasingly relevant in large industrial automation systems.

Integration and Ecosystem Fit

No PLC operates in isolation but is part of a larger automation infrastructure. Nearly every PLC communicates with an operator interface, whether it’s a simple text display, a complete supervisory control and data acquisition (SCADA) system, or an Industrial Internet of Things (IIoT) gateway. Engineers must ensure that the PLC supports common communication protocols for these upper layers. Standard protocols include Open Platform Communications Unified Architecture (OPC UA), Modbus Transmission Control Protocol (TCP), and proprietary protocols such as EtherNet/IP and PROFINET.

Therefore, choosing a PLC that is interoperable with other PLCs is essential. For example, the project might need to integrate a new PLC with an old one that uses only Modbus remote terminal unit (RTU). The new PLC should be able to host a serial Modbus module and act as the master to the old PLC to receive data.

Vendor lock-in is another aspect to consider, and it can have practical implications. Proprietary platforms can limit expansion and support opportunities. Alternatively, a PLC that adheres to widely used industry standards can integrate components from different vendors and migrate if needed.

Conclusion

The PLC has become a core functional unit of the control system. Therefore, the controller must be task-specific, operationally reliable, and resistant to future bottlenecks. By considering factors such as raw specifications, programming environment, performance headroom, and integration, engineers can select a PLC platform that will reliably support the industrial automation systems of tomorrow.

In the final part of this PLC blog series, we look at virtual PLCs and how they enhance scalability by eliminating vendor lock-in.

PLC, PAC, and Industrial PC Architectures for Automation

Mon, 01 Jun 2026 19:11:19 GMT

PLCs, PACs, and industrial PCs form the core of new industrial automation infrastructure

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The industrial automation landscape is defined by deterministic control logic and the increasing data-intensiveness of digital transformation. To support these modern infrastructure needs, various controller architectures are used, such as programmable logic controllers (PLCs), programmable automation controllers (PACs), and industrial PCs.

However, as technology advances, the boundaries between these architectures have blurred. This creates a gray zone in which industrial controller selection depends more on the specific engineering constraints of the deployment. This blog does not aim to declare the best controller for industrial automation infrastructure, but rather to provide a decision framework guided by engineering requirements and trade-offs.

Understanding the Execution Model

The execution model is at the core of an industrial automation system, controlling how processors interact with external physical triggers. The choice among scan-based, task-based, and event-driven models will determine the degree of determinism and their resilience to timing drift.

PLCs operate on a scan-based execution model: They scan the input, execute the program, and update the output. This model provides cyclical determinism, ensuring that each logic instruction is executed exactly once per cycle and that input and output (I/O) states remain consistent throughout execution.

The main advantage of the scan-based execution model is its predictability. For instance, a control engineer can calculate the exact maximum response time for the system. However, a limitation is that the logic execution is tied to the slowest part of the cycle. If communication tasks are not isolated or prioritized appropriately, they can extend the effective scan time and affect control responsiveness.

PACs and industrial PCs are moving away from simple scanning to a task-based or event-driven execution model. In these systems, the logic is organized into tasks with assigned priorities and execution intervals. The operating system (OS) scheduling algorithm handles preemption to allow high-priority tasks to interrupt their lower-priority counterparts.

Even the control application code is executed only in response to a specific trigger, such as a hardware interrupt or a software exception. These approaches optimize computation efficiency by sampling inputs only when necessary. However, these models increase the risk of unpredictable latency if multiple events occur simultaneously.

The OS controls the execution behavior of industrial controllers. PLCs are designed with real-time OS (RTOS) or bare-metal control loops, each optimized for industrial tasks. The firmware minimizes interference from non-control programs, resulting in high reliability for I/O scanning and logic execution.

PACs typically use an RTOS or a real-time execution layer on a general-purpose OS. For instance, a PAC may run a real-time Linux kernel, or a specialized OS that can preempt tasks reliably. Industrial PCs, on the other hand, run general-purpose OSs that are not designed to be real time. However, modern industrial PC control software installs a real-time extension that enables cyclical execution with higher priority than the general-purpose OS.

Real-Time Performance in Motion Control

The most crucial aspect of any industrial automation infrastructure is the control system.

Motion control demands deterministic, high-frequency loop updates (often in the sub-millisecond range) and precise synchronization between axes. Traditional PLCs were not designed for multi-axis motion, and implementing complex motion required either adding separate motion controller modules or using smart drives. This led to fragmentation that was hard to maintain.

Modern PACs and advanced PLCs address this issue by integrating motion-control capabilities into the controller itself. A PAC generates multi-axis trajectories and coordinates axes in real time using standard networking interfaces, such as EtherCAT or SERCOS, to synchronize drives.

On the other hand, a typical industrial PC running Windows would struggle to ensure, for example, a consistent 1kHz update rate to eight servo axes. A non-real-time OS may experience occasional delays measuring tens-of-milliseconds due to scheduling, which is unsuitable in motion control. This constraint is why industrial PCs used for complex motion control are invariably equipped with real-time extensions or specialized motion hardware.

Industrial PCs with proper real-time setup are ideal for motion applications that demand intensive computation or data processing. For instance, a robotic cell that integrates real-time control with machine vision or artificial intelligence (AI) path optimization. A PLC or PAC might not have the computational power to handle vision processing or AI inference, whereas an industrial PC can handle all of it on a single platform.

Decision Framework for Industrial Controllers

When selecting a control architecture, engineers should systematically evaluate the application requirements. A decision framework can be outlined as follows:

Technical Requirements

System engineers must document the project’s key requirements, including the number of I/O points, required logic scan/update rates, motion-control needs, data-handling requirements, integration points, uptime requirements, environmental conditions, and any safety certifications. The requirement analysis should make clear which factors—such as speed, volume, complexity, and reliability—are most crucial.

Complexity and Platform Capability

It is important to understand the project’s complexity and the controller capabilities required to perform the job. For a simpler application, a PLC will meet the requirements at a lower cost and complexity. But if the application requires a complex architecture with multiple I/Os, many axes, and high data throughput, then it likely requires an advanced PAC- or IPC-based solution.

Total Cost of Ownership

Beyond the initial hardware cost, engineers must also account for software licensing, engineering development time, training, and long-term maintenance. For example, the hardware cost of a PLC may be higher per I/O, but the engineering time to implement and maintain might be lower. For large, complex systems, the scalability of an industrial PC or PAC can be an advantage when factoring in the ability to consolidate functions.

Future Planning

Engineers must consider the expected life cycle and expansion of any automation application. If the system is expected to grow or require hardware upgrades, choosing a scalable platform is important. But when the project’s scope is fixed and unlikely to grow, a simpler PLC might be enough to avoid unnecessary complexity.

Conclusion

With modern industrial infrastructure requiring many varied automation demands, there is no one-size-fits-all industrial controller. The architectural choice should be driven by the project’s specific constraints and goals. An engineer’s task is to balance the trade-offs and combine platforms to create an optimal solution. By following a systematic evaluation, one can choose an architecture that is fit for purpose.

In the next installation of this PLC blog series, we take a closer look at the selection criteria driving control system decisions.

Machine Safety Best Practices in Industrial Automation

Mon, 06 Apr 2026 19:23:22 GMT

Implement functional safety, risk assessment, and protective measures in automated systems

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The most critical requirement in an industrial plant, particularly those operating heavy machinery or complex automated systems, is a robust safety program. While modern automation brings significant productivity and efficiency benefits, it also introduces potential hazards to operators, maintenance personnel, and anyone working near automated equipment.

Effective machine safety in industrial automation requires a systematic approach that combines adherence to industry standards, comprehensive risk assessments, and the implementation of engineering safeguards. These safeguards must be integrated into machine design and control systems to minimize hazards while maintaining operational efficiency.

This blog explores machine safety best practices in industrial automation, outlining how risk assessment, functional safety standards, and safety- and performance-level targets guide the design of reliable safety functions. It also explains how common safety equipment and standards-based implementation helps protect personnel while maintaining system performance.

Steps to Implement Machine Safety

Implementing safety in an automation environment starts with a structured functional safety life cycle. This process includes identifying hazards, assessing risks, reducing those risks through engineered safeguards, and continually validating and maintaining machine safety.

International standards provide frameworks for implementing best practices. For instance, International Organization for Standardization (ISO) 12100 gives principles for machinery risk assessment and hazard reduction, while standards such as ISO 13849 and International Electrotechnical Commission (IEC) 62061 define requirements for safety-related control systems.^[1] In process industries, IEC 61508 and IEC 61511 govern functional safety and safety instrumented systems (SIS), respectively.^[2]

Assess Hazards and Risks

Risk assessment begins by identifying potential hazards associated with machinery or processes. These hazards may include mechanical risks (e.g., crushing or pinch points), electrical hazards from energized components, pneumatic or hydraulic pressure failures, or chemical and thermal hazards.

After identifying the hazards, engineers must evaluate the severity and likelihood of each to determine the required level of risk reduction. The objective is to determine whether additional safeguards are necessary and what type of safety function is required.

Standards like ISO 12100 and American National Standards Institute (ANSI) B11.0 guide this process for machinery safety.^[3] For instance, a risk assessment might identify pinch-point hazards on a conveyor system, collision risks in robotic work cells, or overpressure conditions in a reactor vessel. This assessment is intended to determine which hazards require engineered protective measures.

Apply the Hierarchy of Controls

Risk mitigation follows the hierarchy of controls that prioritizes hazard-reduction methods by effectiveness. The preferred approach is to eliminate hazards or substitute safer alternatives during the design stage. If elimination is not possible, engineers must implement engineering controls to isolate operators from hazards. Engineering controls include physical guards, interlocks, safety circuits, and automated shutdown systems that can prevent an incident without relying on human intervention.

Design and Integrate Safety Functions

Once engineers have assessed the hazards, they must implement safety functions within the machine control systems to achieve the required level of risk reduction. Safety functions monitor critical inputs, such as guard position, emergency stop activation, or intrusion detection, and automatically transition the system to a safe state when hazardous conditions are detected.

These functions are implemented using safety-related control systems, which may include safety relays, safety programmable logic controllers (PLCs), or certified motion controllers. Safety functions are designed to be fail-safe, meaning that a fault condition (e.g., a wire break or power loss) causes the system to default to a safe state.

Reliability is increased through redundancy and diagnostics. For example, many safety circuits use dual-channel architectures with cross-monitoring, so that if one channel fails, the other can still trigger a stop.

Functional safety standards quantify reliability using constructs like Safety Integrity Levels (SIL; Table 1) or Performance Levels (PL).

Table 1: SIL determined by probabilities showing the likelihood that a safety function will fail when required^[4]

Safety Integrity Level	Average probability of failure on demand (PFD_AVG) in low demand mode of operation	Probability of failure per hour (PFH) in high demand mode of operation
SIL 1	≥ 10^-2 to < 10^-1	≥ 10^-6 to < 10^-5 per hour
SIL 2	≥ 10^-3 to < 10^-2	≥ 10^-7 to < 10^-6 per hour
SIL 3	≥ 10^-4 to < 10^-3	≥ 10^-8 to < 10^-7 per hour
SIL 4	≥ 10^-5to < 10^-4	≥ 10^-9 to < 10^-8 per hour

A PFD_AVG of 10^-3 (SIL 2) means the safety function has a target average probability of failing once in every 1,000 demands. The engineer must determine whether this level of risk is acceptable for the specific hazard. If the consequences of failure are severe, resulting in injury, explosion, or major equipment damage, SIL 2 may not be sufficient. In such cases, a higher SIL may be required.

Engineers must ensure that this failure rate is sufficiently low for the application’s risk level. This is achieved by analyzing component failure rates (Table 2), common cause failures, and diagnostic coverage to verify that the safety function meets the required target failure probability.

Table 2: Probability of failure during continuous operation^[5]

Performance Level	Average probability of failure per hour (PFH)
PL a	≥ 10^-5 to < 10^-4 per hour
PL b	≥ 3 x 10^-6 to < 10^-5 per hour
PL c	≥ 10^-6 to 3 x 10^-6
PL d	≥ 10^-7 to < 10^-6 per hour
PL e	≥ 10^-8 to < 10^-7 per hour

For instance, a system designed to attain PL d with a PFH of 10^-7 implies that a failure may occur once every 10 million operating hours. Therefore, engineers can use these ranges to determine the risk reduction provided by a safety function and to ensure the system is reliable for the level of hazard exposure.

In contrast to SIL, achieving a PL is not based solely on the probability of failure. While both SIL and PL use probabilistic targets, ISO 13849 requires that the target PL be achieved through a combination of system architecture (Category), component reliability according to mean time to dangerous failure (MTTFd), diagnostic coverage, and protection against common cause failures.^[6]

Ensure Standards Compliance and Certification

Compliance with safety standards is important for reducing risk and meeting regulatory and legal requirements.

In the United States, regulations from the Occupational Safety and Health Administration (OSHA) mandate practices such as machine guarding and lockout/tagout (LOTO) procedures to control hazardous energy.^[7] In the European Union, machines must comply with the Machinery Directive and meet CE marking requirements by adhering to harmonized standards.^[8]

Safety components must also be certified by independent organizations, such as the German-based TÜV, to verify compliance with functional safety standards.^[9] For instance, a safety controller may be TÜV-certified for use in applications up to SIL 4 or PL e, indicating that the device meets the highest reliability criteria defined by IEC 61508 and ISO 13849.

Validate and Maintain Systems

After implementing safety measures, engineers must validate the automation systems to confirm that all safety functions operate as intended. Validation ensures that sensors, interlocks, emergency stop devices, and other safety mechanisms detect hazards and initiate safe shutdown procedures. In many systems, engineers must perform formal verification tests to confirm that safety functions achieve the required SIL or PL targets.

Safety systems also require ongoing maintenance and periodic testing. Preventive inspections ensure that safety devices remain operational, properly calibrated, and free from bypasses or unauthorized modifications. For example, maintaining an SIS in a process plant requires technicians to routinely proof-test sensors, logic solvers, and shutdown valves to ensure they will respond correctly during an emergency.

Machine Safety Equipment

Companies use a wide range of safety devices and control components to implement protective measures in industrial automation systems. These devices are designed to either prevent operators from entering hazardous areas or to detect unsafe conditions and automatically stop the automated system.

Physical Guards and Safety Interlocks

Physical guards are among the most common safety measures used in industrial machines. Fixed barriers, such as metal or polycarbonate enclosures, prevent operators from accessing moving parts or hazardous mechanisms.

For machinery that requires regular access for maintenance or operations, interlock switches ensure that the machine stops before the guard can be opened. For example, a computer numerical control (CNC) machine may include an interlocked door mechanism that immediately cuts power to the spindle and axes when the door is opened.

Interlock devices come in several forms, including switches, relays, and logic-controlled devices. All serve the same function of linking the machine’s operating state to the position of a physical guard.

Emergency Stop Devices

Emergency stop (E-stop) devices are mandatory on most industrial machines. These are red panic buttons that allow operators or nearby personnel to shut down the equipment immediately in an emergency.

E-stop devices are usually integrated into the machine’s safety control circuit and monitored by safety relays or controllers. In large or linear machinery like conveyors, E-stop pull cords may also be installed to allow workers to stop the equipment from any point along the system.

Safety Mats and Edges

Safety mats and safety edges provide contact-based protective sensing for industrial environments.

A pressure-sensitive safety mat contains embedded sensors that detect the presence of a person standing in a monitored zone. If weight is applied to the mat, the safety control system stops the machine right away. These devices are commonly installed around robotic work cells or hazardous machinery where physical guarding is impractical.

Safety edges are used on moving equipment, such as automated guided vehicles (AGVs), machine doors, or powered gates. When the edge makes contact with an object or person, the system triggers a stop or reversal motion to prevent injury. These contact-based solutions are used alongside non-contact protective devices like safety light curtains or laser scanners.

Controllers and PLCs

Safety controllers serve as the decision-making element of modern safety systems.

Simpler machines may use dedicated safety relays that monitor safety inputs (e.g., emergency stops, guard switches, light curtains). These relays detect faults with built-in diagnostic functions, including pulse testing and feedback-loop monitoring.

More complex automation systems rely on safety PLCs designed to meet functional safety requirements. These devices incorporate features such as redundant processors, continuous diagnostics, and certified safety logic blocks.

Safety PLCs allow multiple safety inputs and outputs to be evaluated using validated safety logic. This enables engineers to implement complex safety strategies across large automated systems while maintaining compliance with functional safety standards.

Conclusion

Machine safety relies on a comprehensive combination of risk assessment, engineered safeguards, and standards-based control system design. By following the structured safety methodologies and implementing certified safety devices, engineers can significantly reduce operational risk in dynamic automation environments.

[1] https://www.iso.org/standard/51528.html; https://www.iso.org/standard/73481.html; https://webstore.iec.ch/en/publication/59927
[2] https://assets.iec.ch/public/acos/IEC%2061508%20&%20Functional%20Safety-2022.pdf; https://webstore.iec.ch/en/publication/24241
[3] https://webstore.ansi.org/standards/amt/ansib112023
[4] https://assets.iec.ch/public/acos/IEC%2061508%20&%20Functional%20Safety-2022.pdf
[5] https://www.iso.org/standard/73481.html
[6] https://www.iso.org/obp/ui/en/#iso:std:73481:en
[7] https://www.osha.gov/laws-regs/regulations/standardnumber/1910/1910.212; https://www.osha.gov/control-hazardous-energy
[8] https://osha.europa.eu/en/legislation/directives/directive-2006-42-ec-of-the-european-parliament-and-of-the-council; https://europa.eu/youreurope/business/product-requirements/labels-markings/ce-marking/index_en.htm
[9] https://www.tuv.com/world/en/

Machine Safety Risk Assessment for Safer Industrial Automation

Wed, 18 Mar 2026 15:47:19 GMT

Structured risk assessment makes industrial automation fundamentally safer.

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Industrial automation brings both opportunity and risk. Powerful robots, conveyors, and control systems can dramatically increase productivity, but they also introduce hazards that can injure human operators or damage equipment if not properly controlled.

To manage these hazards, global and national bodies—including the International Organization for Standardization (ISO), International Electrotechnical Commission (IEC), and International Society of Automation (ISA)—provide guidelines that help engineers develop robust safety protocols. In the first part of this blog series on automation safety, we laid out critical safety standards and certifications that safeguard machines and workers operating in industrial environments. Here, we dig deeper into a core practice of every safety program: machine safety risk assessment. This is a structured method for identifying and mitigating hazards before they lead to accidents, and it is key to industrial automation delivering the efficiency required in today’s manufacturing facilities.

Advantages of a Safety Risk Assessment

Even in highly automated settings, humans still interact with machines for setup, operation, or maintenance. A formal risk assessment provides a consistent approach to identifying hazards, analyzing the risks, and defining preventive measures. The outcomes of this approach benefit several areas of automation.

Prevent Accidents and Injuries

Industrial environments, from factories to construction sites, can be hazardous, especially when large machines are in use. Therefore, a risk assessment helps predict and address potential problems. For example, detecting pinch points on a conveyor or the collision risk of a robotic arm allows teams to design safeguards, such as barriers, sensors, or access-control zones.

Minimize Downtime and Costs

Unplanned machine failures and safety incidents halt production and increase maintenance costs. A risk assessment examines where things might go wrong (e.g., a worn-out sensor that could fail, unsafe interactions between a human and a robot). By identifying potential issues before they cause breakdowns or injuries, organizations can reduce emergency stops and repairs.

Meet Regulatory and Legal Requirements

Most jurisdictions require employers to assess and mitigate workplace hazards. Performing a machine risk assessment helps organizations meet safety standards and legal obligations and provides documentation to prove compliance. In the European Union, a machine cannot be legally sold or receive a CE marking without a documented risk assessment demonstrating that all relevant safety directives have been addressed.

Protect Long-Term Operations

Regular risk assessment is an investment for the future. As technology evolves and machinery becomes more complex and automated, periodic assessment helps organizations adapt by identifying new hazards or degraded safety measures over time. This proactive approach safeguards both the workforce and the equipment.

Conducting a Machine Safety Risk Assessment

Performing a risk assessment for an industrial automation environment involves a systematic approach. Standards such as ISO 12100 (Safety of Machinery—General Principles for Design) provide a well-defined framework for this process.

Determine the Limits of the Machinery and Identify Hazards

The first step in any risk assessment is to define the machine and its use boundaries. This means describing its intended functions, operating environment, and complete life cycle, from installation and operation to maintenance and decommissioning. It also involves specifying who interacts with the equipment (e.g., operators, maintenance crews) and under what conditions.

Next, establish the different types of limits that define how the machine operates safely.

Use limits: what the machine is designed to do, and what constitutes misuse
Space limits: physical area available, including clearances, access zones, and restricted regions
Time limits: expected operational lifespan, maintenance intervals, or cycle times
Other limits: materials processed, environmental factors (such as temperature, humidity, lighting), and any special industry-specific constraints

In an automated warehouse setting, for example, defining a fleet of automated guided vehicles (AGVs) requires detailing their intended routes, load capacities, and interaction rules. Space limits would capture pedestrian exclusion zones and narrow-aisle constraints. Other limits could involve floor conditions, such as avoiding steep ramps or wet floors that increase the risk of skidding.

Once these operational boundaries are defined, engineers can systematically identify all reasonably foreseeable hazards. This part of the assessment involves examining each machine component, every operational phase, and every task performed by operators or maintenance personnel. For an AGV system, likely hazards include collision with workers or other vehicles, falling objects from storage racks, and conveyor belt entrapment.

Estimate the Risks of Each Hazard

For each identified hazard, assessors then estimate the associated risks. According to ISO 12100, risk is a combination of the severity of potential harm and the probability of its occurrence. Together, these measurements provide a risk level, which can be done qualitatively or quantitatively. Many organizations use a risk matrix or a numerical scoring system to rank risks.

A common approach is to assign a severity level (1 to 3) and a probability level (1 to 3) to the hazard. The risk might be the product of those values, or it might be a look-up table. ISO 12100 does not mandate a specific matrix, but it emphasizes considering the factors as objectively as possible.

For example, in the event of a collision between an AGV and a pedestrian, severity can range from moderate (fall injury) to high (fatality). The probability of a busy warehouse exposing pedestrians to AGVs is high, but modern sensors can help reduce the risk level. Still, such an event is possible if sensors or rules fail. The risk may be considered high, warranting reduction through better design principles.

Evaluate and Reduce Risk

Once the assessors have estimated each hazard’s risk level, they evaluate whether the risk is acceptable or if it needs to be reduced. In many cases, all but the most trivial risks will require some reduction because regulations demand a high safety standard for machinery. It is common at this stage to prioritize risks by addressing the highest risks first and ensuring critical hazards are mitigated.

Since the AGV collision example is rated as high risk, the organization would be required to implement controls such as adding safety laser scanners and traffic management software. ISO 12100 prescribes a hierarchical three-step method for risk reduction.

Inherently safe design measures
Safeguarding and complementary protective measures
Information for use

This hierarchy reflects the principle that organizations should address hazards through design changes before resorting to guards or warnings.

Returning to the AGV collision example, the organization can address the collision hazards by redesigning the warehouse layout to minimize the number of crossing paths between humans and AGVs. The AGVs can also have a specified maximum speed limit and controlled acceleration, so even if a collision occurs, the impact is less severe. In addition, each AGV can be equipped with a 360° laser safety scanner that will slow or stop the vehicle if an obstacle is detected within a specific range.

Post-Assessment Activities

Completing the machine risk assessment and risk reduction steps is not the end of the safety process. Organizations must document the risk assessment, implement the recommended controls, verify their effectiveness, and establish a continuous safety improvement cycle.

ISO 12100 highlights that risk assessment is iterative and should be maintained throughout a machine’s life cycle. This means that even after the machine is delivered and running, the process can restart if circumstances change. For example, if a machine is moved to a new facility, the organization should perform a new review of risks. Or if a machine’s production rate increases, re-evaluate the risk levels.

By continually updating the risk assessment, organizations ensure that documentation remains current and the safety measures remain effective.

Conclusion

Risk assessment is not just a paperwork exercise, but a core engineering tool that saves lives, minimizes downtime, and ensures compliance. By adopting a structured approach of defining limits, identifying hazards, and applying the hierarchy of risk reduction measures, engineers can create and maintain safe and reliable industrial automation environments.

In the final part of this automation safety blog series, we detail the best practices for implementing functional safety, risk assessment, and protective systems in industrial automated systems.

Machine Safety in Industrial Automation Environments

Sat, 07 Mar 2026 00:17:38 GMT

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In high-risk industries, such as manufacturing, energy, and mining, machines often concentrate immense mechanical, electrical, thermal, pneumatic, or chemical energy sources. A minor design flaw, software bug, or operator error can cascade into catastrophic consequences.

According to the National Safety Council, in the United States alone, over 4 million medically consulted work injuries and 4,500+ fatalities were recorded in 2023.^[1] These statistics underscore why machine safety is non-negotiable in any automation environment. It is now a core value and design principle that must be built into every process and system.

From a design perspective, modern automation integrates functional safety to prevent accidents. This could include implementing redundant, fail-safe controls, so that if a device malfunctions or the operator makes an error, the system detects the danger and automatically moves to a safe state. For example, a robot may have light curtains or laser scanners that halt motion if a person steps too close, or a chemical reactor may have automated shutdown interlocks if temperature or pressure falls outside a safe range. International safety standards define this approach through a risk-based framework for the design of safety-related electrical and electronic systems.

This blog lays out some of the standards and certifications from organizations that ensure industrial automation moves forward with protections in place for both machine and worker.

Safety Standards Organizations

Across all industrial environments, safety must be engineered into technology and ingrained in work practices. This is achieved by adhering to well-established standards and guidelines throughout the design, operation, and maintenance of equipment. The following are major organizations that develop these safety standards and the scope of their influence.

International Organization for Standardization

The International Organization for Standardization (ISO) is a global, non-governmental standards organization that develops and publishes a wide range of international standards across all industries. For the safety of machinery and industrial equipment, ISO has several important standards, including ISO 12100 and ISO 13849.

ISO 12100: This foundational standard defines a systematic methodology for engineers to identify hazards, estimate risks, and implement protective measures to keep risks as low as reasonably possible.
ISO 13849: This standard provides requirements and guidance on designing the parts of control systems that integrate safety functions. It specifies how to validate Performance Level (PL) for those functions, which has ratings from a (low risk) to e (high risk), as well as determine required PL (PL_r) for parts of the control system. For example, a machine’s emergency stop circuit might be required to meet PL e (high performance, typically dual channel with self-monitoring).

International Electrotechnical Commission

The International Electrotechnical Commission (IEC) is a global standards body that focuses on electrotechnical and electronic standards. In the context of safety, IEC’s domain covers all electrical and electronic aspects of equipment, ranging from circuit breaker specifications to the safety of complex automated systems.

One of the most important safety standards is IEC 61508, the core functional safety standard that provides a comprehensive lifecycle for the design, implementation, and maintenance of systems that perform safety functions.

Beyond functional safety, IEC also publishes critical hardware-oriented safety standards. For instance, IEC 60204-1 specifies electrical safety requirements for machinery, including proper grounding and emergency-stop circuitry. The IEC 62061 series covers the functional safety of safety-related control systems. These specifications apply to machine control systems and align with the IEC 61508 framework.

International Society of Automation

The International Society of Automation (ISA) is a US-based nonprofit professional association developed by international experts, and its standards are approved and accredited by the American National Standards Institute (ANSI). The most crucial ISA safety standard is ISA-84, which is equivalent to IEC 61511 (i.e., functional safety for process industries) and covers safety instrumented systems (SIS) in the process industry.

The ISA-84 standard provides detailed guidance for every phase of the safety lifecycle, from initial risk assessment and Safety Integrity Level (SIL) determination to the design and engineering of the SIS, through maintenance, testing, and eventual decommissioning. One important aspect of this standard is that it emphasizes creating a safety requirements specification and maintaining documentation to ensure the SIS is appropriately managed over the long term.

Making Safety Standards Work in Practice

Having all the standards on paper is one thing, but it is vital to ensure that real-world industrial systems and facilities meet them. This is where certification, testing, and conformity assessment come into play. Various organizations and schemes exist to certify that a component, machine, or system complies with the relevant safety standards.

One familiar example is the CE marking in the European Union (EU). CE marking is a regulatory conformity mark that indicates a product meets the essential requirements of all applicable EU directives.^[2] To affix a CE mark, manufacturers must ensure their product complies with European standards, many of which are ISO/IEC standards. They often must have an independent Notified Body conduct tests or reviews of certain high-risk products.

In North America, certifications from organizations such as UL Solutions and the CSA Group are considered standard. A UL listing or CSA mark on equipment indicates the organization has tested the product and found it compliant with their relevant safety standards, which often align with ANSI/IEC standards. For example, having a UL 508A label means that the industrial control panel’s construction and components meet fire and shock protection requirements. While UL certification is not required by federal law in the United States, many local codes and industrial customers do need it.

Cost of Non-Compliance

Of course, complying with safety standards and regulations is not just about avoiding fines. It’s fundamentally about protecting lives. However, when organizations do cut corners and neglect safety, the consequences can be severe.

The first and foremost consequence is the human cost of safety failure. But companies that forget this can also expect to pay very steep financial penalties. Regulators worldwide impose fines and legal liability on businesses that violate safety laws and standards, especially if negligence is proven.

For example, in the United States, the Occupational Safety and Health Administration (OSHA) routinely inspects workplaces and issues citations. The fines per violation can range in the tens of thousands of dollars—sometimes more. A 2023 OSHA case investigation showed a manufacturer in Alabama was fined nearly US$185,000 after a 20-year-old worker was killed while caught in an unguarded machine part.^[3]

Conclusion

Safety in automation and industrial environments is a complex ecosystem that involves strict standards, diligent engineering, continuous training, and third-party oversight. But everything boils down to a simple truth: Every worker’s life is priceless, and every accident is preventable with the proper precautions.

With this foundational understanding of the regulatory bodies and safety standards that keep workers and machines safe, the next blog in this automation safety series examines the risk assessment procedures that ensure that safety is maintained in industrial facilities.

[1] https://injuryfacts.nsc.org/work/work-overview/work-safety-introduction/
[2] https://europa.eu/youreurope/business/product-requirements/labels-markings/ce-marking/index_en.htm
[3] https://www.dol.gov/newsroom/releases/osha/osha20230705

Best Practices for HMI Design in Industrial and Safety-Critical Applications

Fri, 30 Jan 2026 18:01:00 GMT

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The consequences of a poorly designed human-machine interface (HMI) can range from lost productivity to human harm. Especially in high-stakes industries like energy, manufacturing, and process automation, HMIs that fail to accurately display critical system information or receive operator feedback present a risk of catastrophic accidents.

However, when engineers approach an HMI design with the same rigor as any other critical system component, the outcome is far different. Part of this approach involves addressing environmental challenges, which we discussed in the previous part of this blog series on HMIs.

This blog explores how utilizing best practices for a well-designed HMI turns these tools into an extension of the operator, reducing cognitive load, enhancing decision-making, and minimizing the risk of human error.

Core Design Principles

Engineers can achieve high accuracy and usability by following core design principles, such as user-centric and task-oriented layouts, error handling and alarm management, and robust hardware design. Together, these elements ensure that the interface is functional and performs reliably in real-world conditions.

User-Centric and Task-Oriented Design

In user-centric design, HMIs are created to suit the operator's needs, tasks, and mental models rather than the system's technical implementation. This means involving actual operators early in the design process—through research, co-design workshops, and usability testing—to ensure the interface aligns with how they think and work.

A task-oriented HMI design presents information and functions in a way that supports an operator's situational awareness and decision-making by reducing visual clutter and cognitive load. To do this, engineers often deploy high-performance graphics with dynamic process visuals that display information in a context-aware manner.

The ISA-101 HMI design standard recommends a hierarchical display organization, from high-level overviews to detailed control screens, to underpin the interfaces’ control elements.^[1] The standard also suggests using gray-scale backgrounds and low-contrast colors, reserving brighter colors only for alarms and critical data.

Error Handling and Alarm Management

HMIs should actively support operators by providing clear feedback and minimizing the chance of errors. In an ideal scenario, the HMI and the underlying control system should be designed with built-in protections to make it difficult for a user to make a mistake. For example, the user interface (UI) can disable controls that are not valid in the current context, preventing inadvertent inputs.

In safety-critical operations, HMIs often include confirmations for actions that have significant consequences. For instance, an HMI might require the operator to verify a command that will shut down a running process, or a control panel may incorporate two-step activation for an emergency alert to avoid accidental triggers.

Alarm management is another facet of feedback and error handling in industrial HMIs. Best practices for alarm management include prioritizing and filtering alarms and providing clear on-screen indications of their status. For example, suppose the temperature of a storage tank rises above a certain threshold. In that case, the interface should display a message such as "Tank 7: High Temperature" with an alarm icon that allows the user to click for details or navigate to the relevant screen.

Environmental and Hardware Design Principles

Industrial HMIs do not exist in a vacuum, but instead must operate reliably in harsh real-world environments. One key consideration is the hardware's environmental ratings. For example, an HMI panel might need a high ingress protection (IP) rating against water and dust in a factory washdown area or on an outdoor oil rig. Ruggedization of these HMIs is crucial, involving shock and vibration mounting, electrical noise filtering, and power conditioning to handle surges or outages.

Lessons from Past Failures

Even with these best practices in mind, a failure to implement them correctly can have severe consequences, as seen in the following case studies. Throughout industries, there have been numerous instances where HMI designs have contributed to accidents or serious problems.

USS Fitzgerald* Collision (2017)*

In 2017, the USS Fitzgerald collided with the MV ACX Crystal commercial container ship, killing seven Fitzgerald sailors. The investigation concluded that sailors on the bridges of many US Navy vessels could not properly operate touchscreens in the ships' control systems. These complex touchscreens became a significant point of failure that contributed to the accident.^[2]

Critical engine controls were difficult to find within the touchscreen menu and lacked the tactile feedback of the physical levers, leading to operator mistakes in steering and speed control. Additionally, HMI touchscreen designs varied among US Navy ships, causing further confusion.

HMI design must ensure usability and context in high-stress settings. Simplicity and familiarity can help avoid such accidents in dynamic situations like these.

Hawaii Missile Alert (2018)

In 2018, a Hawaii Emergency Management Agency employee accidentally sent the public a real missile warning instead of a test message. The operator clicked the wrong option and confirmed it without realizing the mistake.^[3] The system's UI had multiple design flaws, including poorly differentiated options in the menu where the real alert was a near-identical option to the test alert. This lack of sufficient confirmation and a clear way to undo the action highlights critical flaws in error-prevention design.

Conclusion

Designing an HMI is more than just creating screen layouts; it requires trust between humans and machines. Engineers must adhere to design best practices, such as user-centric design, robust error handling, and environmental ruggedization, to ensure that HMIs become reliable partners in complex environments. When the stakes are high, a well-designed HMI improves productivity and protects lives.

[1] https://www.isa.org/standards-and-publications/isa-standards/isa-standards-committees/isa101
[2] https://www.bbc.com/news/technology-49319450
[3] https://docs.fcc.gov/public/attachments/DOC-350119A1.pdf

Environmental Challenges in HMI Design for IIoT Systems

Fri, 30 Jan 2026 15:48:20 GMT

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Human-machine interfaces (HMIs) are becoming increasingly vulnerable to the extreme industrial conditions in which they operate, despite significant advances in their capabilities. In the first part of this blog series on HMIs, we delved into HMI fundamentals and revealed the value they bring to automation systems. From semiconductor labs to harsh factory floors, these industrial settings present unique operating conditions—such as temperature, moisture, vibration, and chemical exposure—that engineers must consider when designing an HMI.

At the same time, the rise of the Industrial Internet of Things (IIoT) has transformed HMIs into intelligent decision-support systems. When enterprises expand HMI deployments across multiple sites, they must ensure these devices are resilient to environmental conditions. This blog details some of the HMI design considerations associated with such harsh environments.

Unique Operating Environments Present Unique Challenges

Designing an efficient and high-performing industrial touchscreen HMI requires a clear understanding of the diverse operating environments. These conditions vary between factory floors, pharmaceutical labs, and steel plants to semiconductor fabrication labs and outdoor mining sites.

For example, in a metal processing plant, an HMI may be mounted near a furnace, requiring it to withstand high temperatures without failing. In a food or pharmaceutical manufacturing plant, regular washdown and sterilization procedures expose HMIs to high humidity, pressurized water, and cleaning chemicals.

The range of operating conditions in industrial environments makes it clear that designing an HMI without considering its operating context can lead to premature failure, reliability problems, and capital loss.

How Environmental Factors Affect HMI Design?

With the diverse range of functions offered by HMIs, the hardware design, material selection, and protective measures used in them can vary widely. Additionally, each of these design choices are directly influenced by several environmental factors common to industrial settings.

Extreme Temperature

Prolonged exposure to extreme temperatures can degrade HMI performance due to mechanical failure, cracked overlays, or electronic faults. To address this, industrial HMIs require an extended temperature rating of −25°C to +70°C and specialized components to ensure thermal stability. Some systems integrate passive cooling mechanisms, such as heat sinks and thermal pads.

Moisture and Humidity

Moisture ingress can short-circuit electronics, obscure displays, and compromise enclosure seals. Even a single gap in the seal can allow water to enter the internal components. Engineers must design HMIs with sealed gaskets, conformal coating, and ingress protection ratings of IP65, IP66, or higher to prevent internal condensation and maintain long-term enclosure integrity.

Chemical Exposure

Chemical incompatibility between the materials used to manufacture HMIs and cleaning agents, disinfectants, acids, and solvents can lead to rapid deterioration and safety hazards. Engineers should design HMIs using stainless steel, coated aluminum, and seals made of fluorosilicone to resist chemical degradation.

Vibration and Mechanical Stress

HMIs mounted on heavy machinery that generate vibration can lead to failures or loosened connections within the equipment. To withstand this, HMIs must feature shock-resistant enclosures and vibration-tested connectors (often built to MIL-STD-810G or equivalent standards).

HMIs are prone to failure when manufacturers and operators fail to account for these different operating conditions. This unreliable performance can hinder efforts to achieve digital transformation in rapidly evolving automated industries.

HMI in the Age of IIoT

In addition to resilience to the operating environment, HMIs are expected to be more intelligent and connected as part of Industry 5.0. They have evolved beyond local displays to become integral parts of the larger network of smart devices. Modern HMIs display information from IIoT systems, providing engineers and factory floor operators access to analyzed data for more informed decisions.

These HMIs communicate with IIoT systems using OPC (Open Platform Communications), UA (Unified Architecture), MQTT (Message Queuing Telemetry Transport), or Modbus TCP (transmission control protocols). They also enable remote access through secure applications and support edge processing and localized analytics. HMI devices must not only maintain environmental and cyber-physical robustness but also handle firmware updates, remote sessions, and continuous data flow—all without compromising uptime. Failure caused by harsh environments means failure across IIoT systems that create the essential connections vital to automated operations.

Conclusion

Today’s HMIs are no longer passive interfaces. They are intelligent, distributed endpoints that must operate in harsh environments characterized by temperature extremes, chemicals, and vibration. At the same time, IIoT integration raises the bar for connectivity, data accessibility, and dynamic response.

In the final part of this HMI blog series, we look at how HMI design best practices help ensure safety while adding efficiency to automated systems.

Understanding the Fundamentals of HMIs

Thu, 29 Jan 2026 21:00:41 GMT

HMI Fundamentals: Enhancing Industrial Automation with Human-Centric Design

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Human-machine interfaces (HMIs) act as gateways between human operators and complex machinery in industrial environments. As automation becomes more present across industry settings, these devices facilitate the use of machine and sensor data to help operators use industrial control systems efficiently.

This blog explores different types of information displayed on HMIs and describes the functionality of passive displays and active control hubs. Additionally, it discusses the importance of human factors in designing HMIs.

Types of Information Displayed on HMIs

HMIs help provide operators with a complete view of their industrial processes, allowing them to maintain operational continuity. These devices achieve this by using sensor data that is transformed into insights for quick decision-making. HMIs display two critical types of information: real-time operational data and historical data with trends.

Real-time operational data is the most fundamental information that HMIs display. These data include sensor parameters such as temperature, pressure, and flow rate, which are crucial for preventing equipment overheating and failures. Such data provides insight into the system's current state, enabling a prompt response to changing conditions.

HMIs can also record historical data from sensors to provide a better understanding of performance. By tracking these data, operators can monitor process efficiency over time. For example, if a robotic arm consistently logs underperforming data, operators can review this information to identify patterns and use the data to schedule future maintenance that can help reduce downtime.

Actions Performed via HMIs

HMIs are not just passive displays. They are interactive control hubs that allow human controllers to perform a wide range of actions for controlling and managing industrial systems. From simple actions like starting equipment to complex actions that affect the entire production line, HMI controls help optimize processes and respond to dynamic operational demands.

Most HMIs include intuitive graphical user interfaces (GUIs), which make it easier for operators to monitor production processes and adjust parameters. Operators can fine-tune process variables, such as motor speeds or sensor recalibrations, to modify the target values displayed in elements such as icons and buttons.

Another key action is automation management and execution, which are crucial functionalities for modern industrial systems. Automation management allows operators to create new procedures that define the order of equipment execution, including predefined values for variables and schematics of the systems used to manufacture a product. For instance, managing multiple automations in a production line can optimize variables that lead to enhanced manufacturing efficiency. Using HMIs, operators can quickly switch between automations for manufacturing variants by configuring the entire production line with predefined settings.

Human Factors to Consider

HMIs' effectiveness in an industrial setting depends on how well they are designed and implemented. Any design that overlooks human factors can lead to significant errors, inefficient processes, or far more severe consequences.

One key human factor is minimizing cognitive load, which directly reduces the mental effort required for operators to understand the interface and information displayed. Without careful design, HMIs can overwhelm users with excessive information provided all at once, hindering decision-making and leading to operational failures and safety hazards.

HMI manufacturers can mitigate this issue by designing with visual hierarchies and color-coded status indicators. A good design presents only necessary information at any given time, making it easy for operators to find the information they need.

Other factors are ergonomics and physical interaction, including screen size, touch sensitivity, button placement, and display resolution. Proper resolution and screen sizes help reduce eye strain during long shifts, while responsive touch sensitivity prevents errors caused by accidental presses. HMIs with properly designed physical and logical aspects allow operators to use the devices for extended periods without fatigue.

Conclusion

HMIs are critical components in the digital transformation of industries. As the adoption of automation and robotics increases, HMI panels will become more necessary, with these complex systems being controlled and managed through such interfaces.

The fundamentals of HMIs and their associated human-centric design considerations ensure a safe and user-friendly experience for operators in the complex world of industrial automation.

In the next part of this blog series, we detail the environmental challenges of designing HMIs for harsh conditions—from extreme temperatures and moisture to chemical exposure and mechanical stress.