5G: literally means the fifth-generation technology standard for mobile networks. It is the successor to 4G, and promises significant performance improvements, largely in the form of data transmission speed increases and latency reductions. This is accomplished by using higher frequency radio waves, among other engineering techniques. Further reading here.
Anomaly Detection: the process of building a profile of normal system behavior, and then leveraging data science techniques to detect whether an event is different from the baseline in an important way.
Artificial Intelligence: a field that focuses on the simulation of human intelligence by machines, including devices and/or computer programs that are capable of perceiving their environments and taking actions to achieve specific goals.
Data Historian: a system for capturing data from industrial operations, generally sensor and machine state/activity data, typically in a time series format. Until recently, most of this data sat dormant in facilities, only to be queried after events of interest happened. In recent years, historians have been used as foundations for more advanced analytics programs, and are increasingly used for real-time intelligence and situational awareness. The OSIsoft PI system is one of the dominant products on the market, and could be considered one of the first IIoT platforms.
Digital Transformation: an umbrella buzz-term for the use of digital technologies for business transformation. In the industrial context, it typically refers to the use of digital solutions to improve operational performance, as well as to provide added value to customers via digitally-enabled industrial products.
Digital Twin: the digital analog of a physical machine/system, which helps enable improved visibility and enhanced asset performance.
Distributed Control System (DCS): a computerized system for controlling a process, often with many control loops, in which autonomous controllers are distributed throughout the system, but there is no central operator supervisory control.
Edge: typically implies being at or near the physical location (machine, facility, etc) where data is generated, and the term is often used to imply direct contrast to the cloud: “edge vs. cloud” is a common phrase when discussing connected system architectures. You might (might) consider the backhaul network (fiber, cellular, LoRa, etc) to be the line where the edge ends. Further reading here.
Enterprise Resource Planning (ERP): a suite of techniques and software tools that help companies manage their various business processes such as supply chain operations, human resources, accounting, project management, etc. The ERP system is often a major data input for various analytical systems, or can also be the final place data is sent to for visualization and action. An ERP integration is a very standard component of IoT and Industry 4.0 deployments. SAP, Oracle, and Microsoft dominate the market (in that order) for ERP software solutions.
Gateway: a hardware device that is involved with the acquisition of data from an operating environment, and then the transference of this data to another application or service. The term is used in a lot of different ways, but in many cases indicates the element of a connected system where data moves from the “edge” to the cloud.
Human-Machine Interface (HMI): a user interface or dashboard that enables a human to interact with a machine system. This can be as simple as a touch-screen display mounted on an industrial computer in a facility.
Industry 4.0: indicative of the 4th industrial revolution, typically used in the context of applying digital technologies (data, robots, etc) to manufacturing operations for improvements in productivity and performance. I’m fairly certain most people don’t know what the 2nd and 3rd industrial revolutions implied.
Industrial Internet of Things (IIoT): the application of Internet of Things (IoT) approaches to the industrial ecosystem. Some people use the term specifically to refer to the use of IoT in factories, while others apply it more generically to other industrial verticals (utilities, oil & gas, mining, transportation, etc). At very least, the phrase normally implies a commercial or B2B context.
Internet of Things (IoT): the notion of connecting things besides traditional compute devices to the internet. In an enterprise context, this is generally done to improve internal efficiencies and to generate new business. In a consumer context, this is generally done for fun and convenience. Generally speaking, this is a dumb buzzword that confuses people and often makes people not want to talk to you.
Internet of Things Platform: a category of technical solutions for connecting products and operations to the internet in valuable ways. They provide functionalities across devices, connectivity, applications, and analytics. These platforms were a cornerstone of the IoT hype bubble, and they remain an important part of most IoT deployments today. Further reading here.
IT/OT Convergence: the notion that old-school operational technologies are becoming increasingly digitized, and now interfacing with enterprise IT systems. This creates huge opportunities in terms of operation visibility & efficiency, but also creates serious threats in the realm of cyber security for increasingly connected industrial environments.
Machine to Machine (M2M): an older term describing communication directly between two machines or devices, and in contrast to a machine communicating with a person. In some ways, this term “M2M” has simply been replaced by “IoT”, since they imply largely the same thing. You might say that “IoT” used to be called “M2M”.
Machine Learning: an application (or subset) of artificial intelligence that enables computer algorithms to improve their performance over time by training on data and learning from experience. Machine learning can be applied to predict future events, make decisions without human intervention, and solve various optimization problems… among myriad other use cases.
Manufacturing Execution System (MES): an information system that connects to and documents the manufacturing process, helping operators get better information to enhance productivity. The goal of an MES is to help support workflows and decision-making to optimize production.
Operational Technology (OT): the technologies used to control and monitor industrial processes and environments. The term is increasingly used to imply a contrast to traditional information technology (IT), although the worlds of OT and IT are increasingly merging.
Pay-Per-Use: a business model whereby customers pay for the utilization of a machine or service, as opposed to paying up-front (CAPEX) or a fixed recurring price. The ability for original equipment manufactures (OEMs) to stay connected to their products in the field is enabling these types of business models, although they are not entirely new. Further reading here and here.
Predictive Analytics: a set of data science techniques (often including machine learning) that examines historical and current data in order to make predictions about future events.
Predictive Maintenance (PdM): the approach of monitoring the conditions of a machine to look for indicators of upcoming machine health issues. This can be accomplished with real-time monitoring (for example, with real-time sensing of machine temperature and vibration); or by periodically (in single instances) performing specific measurements of machine elements (like testing the composition of the machine lubricants). Further reading here and here.
Programmable Logic Controller (PLC): a computer that controls industrial machines and processes. PLCs are prevalent across a wide range of industrial environments in nearly all industrial verticals, such as factories, power plants, warehouses, mines, large commercial/residential buildings, and the entire oil & gas value chain. Given their widespread use, PLCs are a key pillar of many IIoT & Industry 4.0 projects.
Remote Terminal Unit (RTU): a device (typically microprocessor-based) that monitors and controls machines in industrial processes, generally located away from the central control station. There is functional overlap between RTUs and PLCs: RTUs tend to be used more frequently in wider geographic systems, while PLCs are used more frequently for local area systems.
SCADA: an acronym that stands for “supervisory control and data acquisition”, essentially describing the use of computers to control and monitor industrial processes and equipment. A PLC might be considered an element of SCADA, while the term SCADA tends to imply the broader architecture.
Time Series Data: a series of data points organized in time order. Data historians capture data in this manner. Basically picture a spreadsheet where the left column has time recorded in milliseconds, and then the subsequent columns show sensor readings (temperature, humidity, etc) recorded at each millisecond.