Condition Monitoring

The process of monitoring a parameter of condition in machinery (vibration, temperature, etc.), in order to identify a significant change which is indicative of a developing fault or maintenance need.


Anormaly Dectection

The identification of items, events, or observations which do not conform to an expected pattern or other items in a dataset. In predictive maintenance, it often involves identifying unusual behavior before a failure occurs.


Predictive Analysis

The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. It’s central to predicting when machinery might fail.


Machine Learning

A subset of artificial intelligence that enables systems to learn from data and improve from experience without being explicitly programmed. It is used in predictive maintenance to model and predict failures.


Artificial Intelligence (AI)

The simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction.


Internet of Things (IoT)

The network of physical objects—devices, vehicles, equipment, etc.—embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. IoT devices are often used to collect data for predictive maintenance.


Failure Modes and Effects Analysis (FMEA)

A systematic, proactive method for evaluating a process to identify where and how it might fail and to assess the relative impact of different failures, in order to identify the parts of the process that are most in need of change.


Failure Mode, Effects, and Criticality Analysis (FMECA)

An extension of failure mode and effects analysis (FMEA), FMECA involves a criticality analysis based on each component’s failure modes to identify those that are most critical to the system’s reliability.


Digital Twin

A digital replica of a physical asset, process, or system that can be used for various purposes including predictive maintenance. The digital twin allows for monitoring, simulation, and analysis without interfering with the actual asset.


Vibration Analysis

A key technique in condition monitoring, this involves measuring the vibration levels and patterns of machinery to detect imbalances, misalignments, or other issues that may lead to failure.



An imaging technique used to visualize the heat distribution of an object or body, typically with infrared cameras. In predictive maintenance, it helps detect hot spots in machinery that could indicate issues like electrical faults, insulation failures, or poor lubrication.


Wear Debris Analysis

The study of particles that are worn off from a machine component and found in the machine’s lubricant or oil. Analyzing these particles helps in understanding the wear and tear of the component, indicating when maintenance or replacement is necessary.


Reliability Centered Maintenance (RCM)

A process used to determine the maintenance requirements of any physical asset in its operating context. It aims to achieve high safety and reliability levels of machinery and equipment at the lowest possible cost.


Preventive Maintenance (PM)

Scheduled maintenance performed on equipment before a failure occurs, based on a specific time or usage interval, to mitigate degradation and reduce the likelihood of failure.


Corrective Maintenance (CM)

Maintenance tasks performed to identify, isolate, and rectify a fault so that the failed equipment, machine, or system can be restored to an operable condition within the tolerances or limits established for in-service operations.


Predictive Maintenance (PdM)

Maintenance practice based on the condition of equipment with the aim to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance because tasks are performed only when warranted.


Proactive Maintenance

Strategies and actions taken to prevent failures from occurring in the first place, often involving the modification of equipment or processes to eliminate failure modes.


Condition-Based Maintenance (CBM)

A maintenance strategy that monitors the actual condition of the asset to decide what maintenance needs to be done. CBM dictates that maintenance should only be performed when certain indicators show signs of decreasing performance or upcoming failure.

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Asset Performance Management (APM)

The methodology and software tools used to optimize the operational performance of assets. APM involves data capture, integration, visualization, and analytics tied together to improve the reliability and availability of physical assets. It reduces operating costs and risk, maximizes capital investments, and improves operational efficiencies.


Total Productive Maintenance (TPM)

A holistic approach to maintenance that focuses on proactive and preventive maintenance to maximize the operational efficiency of equipment. It involves everyone in the organization, not just maintenance personnel.


Maintenance, Repair, and Overhaul (MRO)

All actions that have the objective of retaining or restoring an item in or to a state in which it can perform its required function. These include activities such as inspection, testing, measurement, replacement, adjustment, and repair.


Asset Life Cycle Management

The process of optimizing the profit generated by assets throughout their life cycle, from acquisition to disposal. It encompasses the concepts of asset reliability and maintenance management.


Operational Availability

A measure of the availability of an equipment or system for use, taking into account both the reliability of the item and the efficiency of maintenance support.


Root Cause Analysis (RCA)

A method of problem-solving used for identifying the root causes of faults or problems. RCA is based on the principle that systems and chain reactions are responsible for most issues.


Reliability Engineering

An engineering framework focused on studying and improving the reliability of systems and components. It involves techniques and methods for ensuring that products and systems perform their intended functions under stated conditions for specified periods of time. Reliability engineering is crucial for effective asset management as it helps in predicting and mitigating the risks associated with equipment failure.


Enterprise Asset Management (EAM)

A comprehensive approach to managing an organization’s physical assets throughout their entire lifecycle in order to maximize their use, improve quality and efficiency, reduce costs, and safeguard health and safety. EAM software solutions are often used to manage and track assets, schedule maintenance activities, and ensure compliance with industry regulations.


Life Cycle Cost Analysis (LCCA)

A process to evaluate the total cost of ownership of an asset throughout its life cycle. This analysis helps in making decisions based on cost efficiencies between alternative investments, including initial costs, operation, maintenance, upgrade, and eventual disposal costs. LCCA is key to strategic asset management by ensuring cost-effective spending decisions are made.


CMMS (Computerized Maintenance Management System)

A software system designed to simplify maintenance management. CMMS is a centralized repository where all information related to an organization’s maintenance operations is stored. This includes data about the machinery and assets (like location, specifications, and condition), maintenance schedules, and historical records of past repairs and services.


Data Historian

A software program that records and retrieves production and process data by time; it stores data in a time-series database that can be used for trend analysis, predictive maintenance, and other analytical tasks. Data historians collect data continuously, 24/7, from various sources such as sensors and control systems, usually within industrial settings like manufacturing plants, chemical plants, and power stations.

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Linear Regression

A model that assumes a linear relationship between the input variables (x) and the single output variable (y). It can be used for predicting an output value given input values.


Logistic Regression

Despite its name, it’s a classification model rather than a regression model. It estimates probabilities by using a logistic function, often used for binary classification problems.


Decision Trees

A model that uses a tree-like graph of decisions and their possible consequences. It’s a simple and useful for both classification and regression tasks.


Random Forests

An ensemble learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.


Support Vector Machines (SVM)

A powerful, classification-focused algorithm that finds the hyperplane that best separates data into two classes.


Naive Bayes

A simple probabilistic classifier based on applying Bayes’ theorem with strong (naive) independence assumptions between the features.


K-Nearest Neighbors (KNN)

A non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space.


Gradient Boosting Machines (GBM)

An ensemble technique that produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.


Deep Learning/Neural Networks

A subset of machine learning based on artificial neural networks with representation learning. Deep learning architectures such as deep neural networks, deep belief networks, and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, and audio recognition.


Convolutional Neural Networks (CNNs)

A class of deep neural networks, most commonly applied to analysing visual imagery. They are particularly known for their ability to recognize patterns and structures in images.


Recurrent Neural Networks (RNNs)

A class of neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behaviour. Used in applications like language modelling and translation.


Long Short-Term Memory (LSTM)

A special kind of RNN, capable of learning long-term dependencies. They’re particularly useful for classifying, processing, and making predictions based on time series data.


Generative Adversarial Networks (GANs)

A class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. Two neural networks contest with each other in a game (in the form of a zero-sum game, where one agent’s gain is another agent’s loss).


Principal Component Analysis (PCA)

A technique for reducing the dimensionality of datasets, increasing interpretability but at the same time minimizing information loss.



A type of neural network used to learn efficient codings of unlabelled data. The coding is done by learning a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction.


Reinforcement Learning

A type of machine learning technique that enables an algorithm to learn through trial and error using feedback from its own actions and experiences.


Soft Sensors

Soft sensors are software-based models that can use algorithms to process hundreds of measurements simultaneously, and in doing so, predict parameters that cannot be measured directly. The results can be continuously updated and presented to a process operator.

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PID Controller

A PID controller, which stands for Proportional-Integral-Derivative controller, is a widely used type of feedback control system in automation and industrial processes. It is designed to regulate and control various systems, such as temperature, pressure, speed, and more, to maintain a desirclooed setpoint (target value) by continuously adjusting a control output.


Proportional Control

A type of feedback control where the correction made by the controller is proportional to the difference between the setpoint and the process variable. This is one of the simplest control strategies used to minimize error.


Integral Control

A control strategy where the controller output is based on the cumulative sum of the error over time. Integral control aims to eliminate residual steady-state error that can occur with proportional control alone.


Derivative Control

Involves a control action that is based on the rate of change of the process error, providing a prediction of future errors and enhancing the corrective actions taken by the system.


PLC (Programmable Logic Controller)

A digital computer used for automation of typically industrial electromechanical processes, such as control of machinery on factory assembly lines, amusement rides, or light fixtures.


Ladder Logic

A programming language used to program PLCs. It visually resembles the schematic diagram of relay logic it is used to control.


Boolean Logic

A subdivision of algebra used for creating true/false statements. Boolean logic underpins the operation of switches and gates in electronic devices, including PLCs.


Fuzzy Logic

An approach to computing based on “degrees of truth” rather than the usual “true or false” (1 or 0) Boolean logic on which the modern computer is based.


Bang-Bang Control

An on-off control strategy that switches abruptly between two states. It is a simple form of feedback control typically used where precise control is not necessary.


Feedforward Control

A control strategy that anticipates process disturbances and accounts for them ahead of time, thus improving the regulation of a control system.


Feedback Control

A common type of control logic that adjusts its operation based on the feedback from the process it is controlling. The system responds to differences between the actual output and the desired output (setpoint).


State Machine

A computational model used in designing both computer programs and sequential logic circuits. It is an abstract machine that can be in exactly one of a finite number of states at any given time.


Supervisory Control and Data Acquisition (SCADA)

A system of software and hardware elements that allows industrial organizations to:

  • Control industrial processes locally or at remote locations
  • Monitor, gather, and process real-time data
  • Directly interact with devices such as sensors, valves, pumps, motors, and more through human-machine interface (HMI) software
  • Record events into a log file.



A phenomenon in control systems where the response exceeds its target value or setpoint. This can occur when a system’s output reacts to a change or command in such a way that it temporarily exceeds the desired response before settling. Overshoot is often a sign of excessive system gain or insufficient damping and can lead to stability issues.



The occurrence in a control system where the response falls below its target value or setpoint before stabilizing. Like overshoot, undershoot is indicative of control loop characteristics such as gain and damping but occurs in the opposite direction.



A range of input values in the operation of a control system where no action is taken by the controller, effectively creating a zone of inactivity. This is often used to prevent the system from reacting to small or insignificant changes in input, which helps in reducing wear and tear on the system components and avoids oscillation.



A phenomenon where there is a lag in response exhibited by a body due to changes in the forces affecting it. In control systems, hysteresis can cause a difference in the response for increasing and decreasing signals, potentially leading to variable system behavior.


Gain Stability

Refers to the ability of a control system to maintain consistent output levels despite changes in the gain settings. Stable gain helps in achieving predictable and reliable system performance.


Settling Time

The time it takes for a system to stabilize within a certain percentage of the target value or setpoint after a disturbance or change in input. This metric is crucial for assessing the performance of control systems in dynamic environments.


Rate Limiting

A control action used to restrict the rate at which a process variable can change. Rate limiting is applied to avoid sudden shifts in output that could lead to mechanical stress or system instability.


Feedback Loop

A fundamental component of many control systems where the output or result of a process is used as input (feedback) to control the dynamic behavior of the system. This loop helps in self-regulation and stabilizes the process by adjusting deviations from a setpoint.

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Sticking or Binding

A common failure mode where the valve stem or actuator gets stuck or binds, hindering the valve’s ability to move properly. This issue can disrupt flow control and lead to operational inefficiencies.



This occurs when there is unauthorized fluid flow in the system, which can be through the seat, stem, or body of the valve. Leakage can reduce both the efficiency and safety of the operational process by altering expected fluid dynamics.



The phenomenon where fluid pressure falls below its vapor pressure, leading to the formation and implosive collapse of vapor bubbles within the fluid. This action can erode and damage valve components, potentially causing catastrophic failures.


Erosion and Corrosion

Damage caused by the flow of abrasive or corrosive fluids through a valve, which can degrade internal structures and adversely affect flow control capabilities over time.



The accumulation of particles or debris in the valve, which obstructs the flow path and impedes the valve’s function. Clogging requires intervention for cleaning or extensive maintenance to restore full operational capability.


Actuator Failure

A critical issue where the actuator, responsible for controlling the valve’s position, fails due to electrical or mechanical problems. This may result in the valve being frozen in one position or failing to respond to control signals.


Instrumentation or Control System Failure

Failures within the valve’s instrumentation or control systems, such as sensor errors or controller breakdowns, can lead to improper valve functionality and operational issues.


Seat and Seal Wear

Natural degradation of the valve’s seals and seats over time, which can lead to increased leakage rates and diminished control accuracy.


Vibration and Noise

Unusual mechanical vibrations or noise during valve operation, indicative of potential issues like misalignment, component looseness, or structural integrity problems.


Stem or Disk Fracture

Severe failures where the valve’s stem or disk breaks, necessitating immediate replacement due to the inability to maintain controlled flow.


Inadequate Response Time

Occurs when a valve does not adjust swiftly enough to changes in the control signal, potentially causing delayed process control and system instability.


Seal or Gasket Failure

The breakdown or deterioration of seals and gaskets within the valve assembly, which can result in leaks and disrupt the process flow, compromising system reliability.

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A signal generated by a system that indicates an abnormal condition requiring a response from the operator. It is often a visual or auditory indication designed to prompt immediate action to prevent an undesired consequence.


Alarm Flood

Occurs when the rate at which alarms occur overwhelms the operator, potentially leading to missed or ignored alarms. This phenomenon can severely compromise the safety and efficiency of operations.


Alarm Priority

The relative importance of an alarm in the context of other alarms and operational conditions. Prioritization helps operators to respond to the most critical alarms first.


Alarm Fatigue

A state of overwhelmed or desensitized response due to frequent alarms, many of which may be non-critical. This can lead to slower response times or missed alarms, posing serious safety risks.


Alarm Suppression

The technique of selectively inhibiting alarms during certain conditions to avoid overwhelming the operator. Suppression must be managed carefully to ensure that critical alerts remain prominent.


Alarm Rationalization

A process to review and analyze alarm settings and their necessity with the aim to optimize alarm systems by reducing nuisance alarms and prioritizing important ones. This process helps in maintaining alarm integrity and effectiveness.



A configurable range within which input fluctuations do not cause an alarm state to change. This is used to prevent frequent toggling of alarms due to noise or minor fluctuations around the setpoint.


Alarm Hysteresis

A method to prevent the frequent activation and deactivation of an alarm by requiring a significant change in the monitored variable before the alarm state is reset. This is closely related to the concept of deadband.


First-Out Alarm

An alarm that identifies and logs the first variable that goes into an alarm state during a disturbance or event. This can be crucial for diagnosing the root cause of issues in complex systems.


Alarm Acknowledgement

The action taken by an operator to confirm that an alarm has been seen and heard. Acknowledgement does not usually resolve the alarm condition but notifies the system that the operator is aware of the alert.

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Batch Processing

A method of manufacturing in which the product is created stage by stage over a series of workstations, and in separate batches. This process is beneficial when producing quantities of the same product aren’t large enough to warrant continuous production.


Continuous Process

A production flow that is consistent and operates 24 hours a day. Unlike batch processing, this process is used for high-volume, high-demand products and typically has a higher efficiency rate.


Lean Manufacturing

An operational strategy oriented toward achieving the shortest possible cycle time by eliminating waste. It is based on the Toyota Production System and aims to enhance product quality, reduce production time, and lower costs.


Six Sigma

A disciplined, data-driven approach and methodology for eliminating defects in any process—from manufacturing to transactional and from product to service.



A concept referring to business activities that continuously improve all functions and involve all employees from the CEO to the assembly line workers. It also applies to processes, such as purchasing and logistics, that cross organizational boundaries into the supply chain.


Total Quality Management (TQM)

A management approach to long-term success through customer satisfaction, focusing on the development of products and services that consistently offer value and reliability to customers.


Just-In-Time (JIT)

An inventory strategy companies employ to increase efficiency and decrease waste by receiving goods only as they are needed in the production process, thereby reducing inventory costs.


Process Optimization

The discipline of adjusting a process to optimize some specified set of parameters without violating some constraints. The most common goals are minimizing cost and maximizing throughput and/or efficiency.



The capability of a process to handle a growing amount of work or its potential to be enlarged in order to accommodate that growth. In industrial terms, it refers to the ability of a system to increase its total output under an increased load when resources are added.



The technology by which a process or procedure is performed with minimal human assistance. Automation or automatic control is the use of various control systems for operating equipment such as machinery, processes in factories, boilers, and heat-treating ovens, with minimal or reduced human intervention.

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Cyber-Physical Systems (CPS)

Mechanisms controlled or monitored by computer-based algorithms, tightly integrated with the internet and its users. In Industry 4.0, CPS interact with the physical world and humans in real-time.


Big Data Analytics

The complex process of examining large and varied data sets to uncover information including hidden patterns, unknown correlations, market trends, and customer preferences, which can help organizations make informed business decisions.


Smart Factory

A highly digitized and connected production facility that relies on smart manufacturing. Thought to be the factory of the future and the main aspect of Industry 4.0, smart factories use technology such as IoT, CPS, robotics, and AI to enhance manufacturing processes.



A system of recording information in a way that makes it difficult or impossible to change, hack, or cheat the system. In Industry 4.0, blockchain can secure supply chains, protect IoT devices, and ensure compliance and reliability in distributed environments.


Cloud Computing

The on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. The cloud is a key element of Industry 4.0, providing the infrastructure to collect, store, and process vast amounts of data from industrial operations.


Edge Computing

A distributed computing paradigm which brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. In Industry 4.0, edge computing supports real-time applications that require rapid processing without latency.


Augmented Reality (AR)

An interactive experience where the real-world environment is enhanced by computer-generated perceptual information. In Industry 4.0, AR can be used to provide workers with real-time information to improve decision-making and work procedures.


Additive Manufacturing

Often synonymous with 3D printing, this involves creating a three-dimensional object by adding material layer by layer, which is opposite to traditional subtractive manufacturing methodologies. Additive manufacturing is integral to Industry 4.0 for prototyping and production.


Industrial Internet of Things (IIoT)

A subcategory of IoT, which focuses specifically on the applications of connected devices and smart sensors in manufacturing and industrial sectors. IIoT devices collect, exchange, and analyze data to improve manufacturing efficiency and safety.


Adaptive Control

This advanced method of control system operates in real-time, automatically adjusting machining operations based on feedback from sensors integrated within production equipment. Adaptive control is critical in Industry 4.0 for optimizing manufacturing processes and enhancing productivity by adapting to varying operational conditions without human intervention.

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Integrates infrastructure and application monitoring with incident management using AI to enhance service diagnostics and resolutions.


Generative AI

Technologies that create derived content and solutions from extensive data sources, impacting many business areas including automation and customer experience.


AI Security

Focused on developing defenses against AI-driven threats and ensuring AI operates securely and predictably.


Quantum Computing

Involves computers using quantum-mechanical phenomena to perform operations on data at extremely high speeds.



The application of advanced technologies, including AI and machine learning, to increasingly automate processes and augment human capabilities.


Autonomous Vehicles

Vehicles capable of sensing their environment and moving safely with little or no human input.


Personalized Medicine

Tailoring of medical treatment to the individual characteristics of each patient, which may involve the use of genetic or other biomarker information.


Edge AI

AI algorithms processed locally on a hardware device, reducing the need for data to be sent to a centralized server.


Immersive Experience Technologies

This refers to technologies such as virtual reality (VR) and augmented reality (AR) that create or enhance digital environments where users can interact. These tools can simulate real-world conditions for training, entertainment, or information exploration purposes.


Smart Grids

Technology-driven electrical power systems that integrate renewable energy sources to enhance reliability and efficiency. Smart grids use digital communication technology to detect and react to local changes in usage, improving energy distribution and optimization.

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