Industrial Automation
| Industrial Automation | |
|---|---|
| Use of control systems, instrumentation and machinery to automate industrial processes |
Industrial automation is the application of control systems, instrumentation, computing and machinery to monitor and operate industrial processes with reduced need for direct human intervention. It includes automated production equipment, process-control systems, industrial robots, sensing devices and software used to coordinate industrial operations. Automation may be implemented in individual machines, production lines or larger industrial facilities.[1]
Industrial automation is applied to manufacturing, material handling, chemical processing, power generation, water treatment and other operations in which physical equipment is monitored or controlled. The systems involved range from controllers operating individual machines to distributed networks that coordinate equipment across industrial sites.[1]
Automation does not necessarily involve artificial intelligence (AI). Conventional industrial controllers execute programmed logic, sequences and control algorithms without machine learning. AI-based systems may supplement these established technologies through applications such as visual inspection, equipment-condition assessment, production planning and predictive maintenance.[2][3]
Scope and forms of automation
Industrial automation includes the control of discrete operations, continuously operating processes and manufacturing activities organized into batches. The equipment and methods differ according to the physical process, its operating requirements and the degree of coordination required.[1][4]
Discrete automation
Discrete manufacturing operations involve distinguishable products or components and commonly require coordinated machine movements, material transfer, assembly and inspection.
Programmable controllers and industrial robots can be used to operate individual machines and coordinate production equipment. Examples of automated functions include component positioning, machining sequences, robotic handling and production-line inspection.[1][5]
Continuous process automation
Continuous process automation is associated with industrial operations in which process variables must be monitored and regulated as the process operates.
Control systems can measure variables such as temperature, pressure, flow and liquid level and adjust equipment to maintain specified operating conditions.
Distributed control systems and other industrial control configurations are used in process industries for the coordinated monitoring and regulation of equipment.[1]
Batch process automation
Batch processing involves executing defined production procedures for identifiable quantities of material.
Batch-control systems coordinate procedures, equipment operations and production information. IEC 61512-1:2026 establishes models and terminology for batch and related procedure-oriented control in the process industries.
The standard distinguishes recipe procedural elements from equipment procedural elements. This separation supports the definition of manufacturing recipes without necessarily requiring changes to the procedures implemented by the equipment.[4]
Principles of industrial control
Industrial automation uses control methods to establish, maintain or change the operating state of equipment and processes.
A typical control arrangement involves measuring the condition of a process, evaluating the information against a desired operating condition, and issuing commands to suitable equipment.
Industrial control systems may use logical conditions, timed sequences or feedback-based control algorithms, depending on the task.[1]
Open-loop and closed-loop control
In open-loop control, commands are issued without using feedback from the resulting process response to correct the command.
In closed-loop control, measurements of the controlled process are returned to the controller. The controller uses these measurements to determine whether corrective action is required.
A feedback-control arrangement commonly includes:
- A specified operating target, or setpoint.
- A sensor or measuring instrument.
- A controller that evaluates the measured information.
- An actuator or final control element.
- The physical process being controlled.
Industrial control equipment implements these arrangements using suitable sensors, controllers, communications and field devices.[1]
Sequential and interlocking control
Sequential control coordinates operations according to predefined steps, events or logical conditions.
Interlocking functions restrict the execution of certain operations until specified conditions have been satisfied. Such functions are used in industrial control arrangements to coordinate equipment and prevent incompatible operating commands.
Where these functions serve a protective purpose, they must be considered as part of the relevant machinery or process-safety design rather than assumed to provide sufficient protection merely because an interlock exists.[1][6]
Core technologies
Industrial automation systems use combinations of field equipment, programmable controllers, computer-based supervisory systems and communications infrastructure. Their organization depends on the application and the system architecture.[1]
Sensors and instrumentation
Sensors and instruments provide measurements of process and equipment conditions.
Industrial measurements may include temperature, pressure, position, speed, flow, vibration and electrical quantities. The information obtained can support equipment monitoring, feedback control, alarm generation and maintenance decisions.
The selection and integration of instruments form part of the engineering of the controlled process.[1]
Actuators and drives
Actuators convert control commands into physical actions.
Industrial examples include motors, motor drives, control valves and mechanisms that produce controlled movement or changes in process conditions.
In a feedback-control system, the actuator or final control element changes the physical process in response to a command from the controller.[1]
Programmable logic controllers
A programmable logic controller (PLC) is a programmable control device used to execute industrial control functions.
PLCs receive information from connected devices and execute programmed instructions to determine control outputs. They are used in automation arrangements involving machine operations, sequential control and process monitoring.[1]
IEC 61131-3:2025 defines programming languages for programmable controllers. Its specified languages include:
- Structured Text (ST), a textual programming language.
- Ladder Diagram (LD), a graphical language.
- Function Block Diagram (FBD), a graphical language.
- Sequential Function Chart (SFC), a set of elements used to structure the internal organization of programs and function blocks.
The standard defines syntax and semantics for these languages, along with configuration and communication-related features for programmable-controller systems.[2]
Distributed control systems
A distributed control system (DCS) uses multiple interconnected control components to coordinate an industrial process.
DCS architectures are associated particularly with plant-wide process control, where regulation and monitoring functions are distributed among connected controllers and supervisory equipment.
The division of control functions among components is an architectural characteristic and does not imply that the system operates without human supervision.[1]
Supervisory control and data acquisition
Supervisory control and data acquisition (SCADA) systems support the monitoring and supervisory control of industrial operations.
A SCADA arrangement may collect information from remote equipment, display process conditions, record operational events and transmit supervisory commands.
SCADA systems can be used where equipment is geographically distributed, including operations involving utilities and infrastructure.
SCADA, DCS and PLC systems are related industrial control technologies, but the terms describe different system configurations or components and should not be used interchangeably.[1]
Human–machine interfaces
A human–machine interface (HMI) provides a means for operators to observe system conditions and interact with automated equipment.
HMI functions may include displaying measurements, alarms, equipment status and operational controls. The design of an interface is related to the responsibilities assigned to human operators and the control system.[1]
Industrial networks and communication
Industrial networks exchange measurements, commands, operating states and other information between connected automation components.
Communication systems must be selected with regard to the requirements of the particular control application, including reliability, operating conditions and security.[1]
OPC Unified Architecture (OPC UA) is a platform-independent standard intended to support interoperable information exchange between industrial systems.
Its architecture includes information modelling, client-server services, security mechanisms and publisher-subscriber communication capabilities.
OPC UA can be applied to industrial sensors, controllers, manufacturing execution systems and enterprise applications.[7]
Industrial robotics
Industrial robots are a category of automated machinery used for programmable physical manipulation.
The International Federation of Robotics bases its industrial-robot terminology on ISO 8373:2021, which identifies industrial robots by characteristics including automatic control, reprogrammability, multipurpose manipulation and programmability in three or more axes.[8][5]
Industrial robots can be incorporated into automated manufacturing systems for handling, positioning and processing physical objects. The integration of robots into a wider production system can involve controllers, tooling, sensors, material-handling equipment and protective devices.[5][9]
Robotics and industrial automation are related but are not identical. An industrial automation system may contain no industrial robot, while a robot is normally integrated with additional control and protective equipment to perform a particular industrial application.[1][9]
Manufacturing systems integration
Industrial automation extends beyond the direct control of individual machines.
Manufacturing operations also involve production scheduling, equipment management, quality operations, inventory and information exchange with business systems.
IEC 62264-1:2013 establishes models and terminology for integrating manufacturing operations and enterprise systems.[10]
Manufacturing operations management
The IEC 62264 framework identifies manufacturing operations management as Level 3 and enterprise business activities as Level 4.
It describes interfaces between manufacturing operations and enterprise systems, including associated information and transactions.
Manufacturing operations management encompasses activities related to production, quality, inventory and maintenance.[10]
Manufacturing execution systems
A manufacturing execution system (MES) is associated with the management and execution of manufacturing operations.
MES applications may support production dispatching, work tracking, collection of manufacturing information and operational coordination.
The specific functions and interfaces depend on the implementation. The manufacturing operations management domain described by IEC 62264 provides a framework for organizing such activities and their relationship to control and enterprise systems.[10]
Functional safety and reliability
Industrial control systems may interact directly with machinery, hazardous processes and critical infrastructure. Their engineering must therefore address safety, reliability, system failures and conditions requiring protective action.[1]
Machinery and robot safety
Industrial robot applications are addressed by the ISO 10218 safety standards.
ISO 10218-1:2025 covers safety requirements for industrial robots considered as partly completed machinery, including aspects of design and risk reduction.
ISO 10218-2:2025 covers industrial robot applications and robot cells, including integration, commissioning, operation, maintenance and decommissioning.[11][9]
The requirements for a robot itself and those for the complete robot application are therefore addressed in separate parts of the standards series.
Safety instrumented systems
In process industries, a safety instrumented system (SIS) is used to perform specified safety functions.
IEC 61511-1 establishes requirements for the specification, design, installation, operation and maintenance of safety instrumented systems intended to achieve or maintain a safe process state.
The standard is developed as a process-sector implementation of the functional-safety principles addressed by the IEC 61508 series.[6]
Engineering and maintenance
Industrial automation systems require attention to equipment conditions and operational requirements throughout their useful life.
Engineering activities may include installation, testing, commissioning, documentation, maintenance and modification of control systems.
Research on manufacturing maintenance management has examined how sensing, diagnostics and decision-support tools can assist human maintenance activities. The usefulness of such tools depends on the operational problems being addressed and their integration into maintenance practices.[12]
Operational technology cybersecurity
Operational technology (OT) includes programmable systems and devices that interact with the physical environment through monitoring or control.
Industrial control systems form part of this broader field.
Because OT systems can affect physical operations, their security requirements must account for performance, availability, reliability and safety alongside confidentiality and system integrity.[1]
Threats and vulnerabilities
Industrial automation systems can be exposed to cybersecurity risks through compromised devices, unauthorized access, insecure communications and weaknesses in system management.
Security incidents affecting a control system may interfere with monitoring, operational commands or the availability of industrial processes.
NIST Special Publication 800-82 Revision 3 provides guidance on assessing threats, vulnerabilities and controls for operational technology environments.[1]
Security architecture and controls
Protective measures can include access management, network segmentation, equipment inventories, logging, monitoring, backup arrangements and controlled maintenance of software and system configurations.
The application of cybersecurity controls must take account of industrial operating requirements. Measures that are appropriate for ordinary information systems may need adaptation where they affect the timing, availability or safety of physical processes.[1]
Industrial cybersecurity standards
The ISA/IEC 62443 standards series addresses cybersecurity for industrial automation and control systems.
It covers topics including terminology, security management, risk assessment, secure product development and technical requirements for components and systems.
IEC 62443-3-3:2013 specifies system security requirements and security levels. The standards framework also uses concepts such as zones and conduits in the assessment and design of industrial control-system security.[13]
Artificial intelligence in industrial automation
Artificial intelligence and machine learning can be incorporated into industrial systems to analyze data, interpret sensor observations and support decisions.
Their applications are distinct from conventional automation functions that operate through programmed logic and established control algorithms.
A 2026 research roadmap by Vogl, Cornelius and Jia examined AI and machine learning in smart manufacturing, including industrial sensing, autonomous systems, robotics, digital twins and manufacturing optimization.[3]
Automated inspection
AI-based perception methods can be applied to manufacturing inspection.
Machine-learning models may analyze sensor or image information to identify patterns and detect anomalies relevant to product or process quality.
The performance of such systems depends on the data, models and conditions under which they are evaluated.[3]
Predictive maintenance
Predictive maintenance uses information about equipment conditions to support decisions concerning maintenance needs.
Industrial AI research examines the use of sensor data and analytical models to estimate degradation, detect anomalies and provide information about potential failures.
Predictive methods do not remove the need for validation or human maintenance expertise. Their usefulness depends on whether the information improves decisions in the relevant operational setting.[12][3]
Production optimization
Industrial AI research also considers production planning, scheduling and optimization.
Models may be used to evaluate operating conditions, resource allocation and production-system performance.
The suitability of an optimization method depends on constraints imposed by the physical system, available measurements and operational requirements.[3]
Digital twins and virtual manufacturing
A digital twin is a digital representation or model associated with a physical entity or process.
In industrial applications, digital twins may combine models and information from physical equipment to support monitoring, simulation, prediction and decisions about system operation.
Digital twins are related to industrial automation, but a conventional industrial control system does not require a digital twin in order to operate.[14][1]
Industrial applications
NIST identifies several uses for manufacturing digital twins, including monitoring equipment, evaluating alternative production plans, assessing machine health and supporting virtual commissioning.
Digital-twin development may involve measurement systems, industrial data, mathematical models and simulation methods.[14]
Validation and limitations
A digital model's usefulness depends on its relationship to the physical system it represents.
NIST research identifies data integration, interoperability, model validation and uncertainty quantification as important challenges in creating trustworthy digital twins.
Consequently, the existence of a simulation or digital representation does not itself establish that the model reliably predicts actual industrial-system behavior.[14]
Implementation challenges
Industrial automation projects may involve technical and organizational constraints that affect their design and operation.
Research and institutional guidance identify several recurring considerations:
- System integration: Equipment and software from different sources may need interoperable interfaces and compatible information models.
- Reliability: Physical equipment and control systems must satisfy the availability and performance requirements of their applications.
- Safety: Protective functions and operating procedures must address hazards associated with machinery and processes.
- Cybersecurity: Connected automation components introduce security-management requirements.
- Data quality: Analytics and AI-based applications depend on the suitability of their input data and evaluation methods.
- Personnel and maintenance: Effective deployment depends in part on maintenance procedures, operator responsibilities and the integration of technology with human work.[10][1][12][3]
The design of an industrial automation system is therefore an engineering problem involving physical processes, control equipment, software, personnel and operating conditions. Technical capabilities must be assessed against the requirements of the specific application rather than treated as universally applicable.[1][12]
See also
- Robotics
- Autonomous Robots
- Digital Twins
- Robot Cybersecurity
- Physical AI
- Autonomous Systems
- Embodied AI
- Computer Vision
References
- ↑ 1.00 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10 1.11 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 1.20 1.21 1.22 1.23 Stouffer, Keith A.; Pease, Michael; Tang, CheeYee; Zimmerman, Timothy; Pillitteri, Victoria Yan; Lightman, Suzanne; Hahn, Adam; Saravia, Stephanie; Sherule, Aslam; and Thompson, Michael (2023). Guide to Operational Technology (OT) Security. NIST Special Publication 800-82, Revision 3. Gaithersburg, Maryland: National Institute of Standards and Technology. doi:10.6028/NIST.SP.800-82r3.
- ↑ 2.0 2.1 International Electrotechnical Commission (IEC) (2025). IEC 61131-3:2025: Programmable controllers — Part 3: Programming languages. Fourth edition, 22 May 2025. Geneva: IEC. Official standard.
- ↑ 3.0 3.1 3.2 3.3 3.4 3.5 Vogl, Gregory; Cornelius, Aaron; and Jia, Xiaodong (2026). "2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing". Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing, volume 2. IOP Publishing, 3 July 2026. doi:10.1088/3049-4761/ae5967.
- ↑ 4.0 4.1 International Electrotechnical Commission (IEC) (2026). IEC 61512-1:2026: Batch control — Part 1: Models and terminology. Second edition, 20 February 2026. Geneva: IEC. Official standard.
- ↑ 5.0 5.1 5.2 International Federation of Robotics (IFR). "Industrial Robots: Definition and Classification". IFR, referring to ISO 8373:2021. Official reference.
- ↑ 6.0 6.1 International Electrotechnical Commission (IEC) (2017). IEC 61511-1:2016+AMD1:2017 CSV: Functional safety — Safety instrumented systems for the process industry sector — Part 1: Framework, definitions, system, hardware and application programming requirements. Consolidated edition 2.1. Geneva: IEC. Official standard.
- ↑ OPC Foundation. OPC Unified Architecture — Part 1: Overview and Concepts. OPC 10000-1, Sections 1 and 4. Official specification. Accessed 11 October 2026.
- ↑ International Organization for Standardization (ISO) (2021). ISO 8373:2021: Robotics — Vocabulary. Third edition, November 2021. Geneva: ISO. Official standard.
- ↑ 9.0 9.1 9.2 International Organization for Standardization (ISO) (2025). ISO 10218-2:2025: Robotics — Safety requirements — Part 2: Industrial robot applications and robot cells. Second edition, February 2025. Geneva: ISO. Official standard.
- ↑ 10.0 10.1 10.2 10.3 International Electrotechnical Commission (IEC) (2013). IEC 62264-1:2013: Enterprise-control system integration — Part 1: Models and terminology. Second edition, 22 May 2013. Geneva: IEC. See especially Clause 5 on hierarchy models. Official standard.
- ↑ International Organization for Standardization (ISO) (2025). ISO 10218-1:2025: Robotics — Safety requirements — Part 1: Industrial robots. Third edition, February 2025. Geneva: ISO. Official standard.
- ↑ 12.0 12.1 12.2 12.3 Brundage, Michael P.; Sexton, Thurston B.; Hodkiewicz, Melinda; Morris, Katherine C.; Arinez, Jorge; Ameri, Farhad; Ni, Jun; and Xiao, Guoxian (2019). "Where do we start? Guidance for technology implementation in maintenance management for manufacturing". Journal of Manufacturing Science and Engineering. ASME. NIST publication record.
- ↑ International Electrotechnical Commission (IEC) (2013). IEC 62443-3-3:2013: Industrial communication networks — Network and system security — Part 3-3: System security requirements and security levels. First edition, including Corrigendum 1:2014. Geneva: IEC. Official standard. See also International Society of Automation, ISA/IEC 62443 Series of Standards.
- ↑ 14.0 14.1 14.2 National Institute of Standards and Technology (NIST). "Digital Twins for Advanced Manufacturing". Engineering Laboratory research programme. Official programme description. Accessed 11 October 2026.
| Physical AI and machine identity | |
|---|---|
| Core concepts | Physical AI · Embodied AI · Machine learning · Autonomous systems · Robotics |
| Hardware and systems | Robots · Computer vision · Sensors · Sensor fusion · Edge AI |
| Identity and trust | Machine identity · Device authentication · Robot cybersecurity · Digital identity · Public key infrastructure |
| Applications | Industrial automation · Humanoid robots · Autonomous vehicles · Smart manufacturing |