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The technical meaning of maintenance involves functional checks, servicing, repairing or replacing of necessary devices, equipment, machinery, building infrastructure, and supporting utilities in industrial, business, and residential installations. Over time, this has come to include multiple wordings that describe various cost-effective practices to keep equipment operational; these activities occur either before or after a failure.
Total cost of ownership (TCO) is a financial estimate intended to help buyers and owners determine the direct and indirect costs of a product or service. It is a management accounting concept that can be used in full cost accounting or even ecological economics where it includes social costs.
General Electric Automation and Controls division combines what was formerly known as GE Intelligent Platforms and Alstom's Power Automation and Controls. In 2019, GE Intelligent Platforms was acquired by Emerson Electric and is now called Machine Automation Solutions.
Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. This lack of performance is most often a failure beyond which the system can no longer be used to meet desired performance. The predicted time then becomes the remaining useful life (RUL), which is an important concept in decision making for contingency mitigation. Prognostics predicts the future performance of a component by assessing the extent of deviation or degradation of a system from its expected normal operating conditions. The science of prognostics is based on the analysis of failure modes, detection of early signs of wear and aging, and fault conditions. An effective prognostics solution is implemented when there is sound knowledge of the failure mechanisms that are likely to cause the degradations leading to eventual failures in the system. It is therefore necessary to have initial information on the possible failures in a product. Such knowledge is important to identify the system parameters that are to be monitored. Potential uses for prognostics is in condition-based maintenance. The discipline that links studies of failure mechanisms to system lifecycle management is often referred to as prognostics and health management (PHM), sometimes also system health management (SHM) or—in transportation applications—vehicle health management (VHM) or engine health management (EHM). Technical approaches to building models in prognostics can be categorized broadly into data-driven approaches, model-based approaches, and hybrid approaches.
Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted. Thus, it is regarded as condition-based maintenance carried out as suggested by estimations of the degradation state of an item.
Machine to machine (M2M) is direct communication between devices using any communications channel, including wired and wireless. Machine to machine communication can include industrial instrumentation, enabling a sensor or meter to communicate the information it records to application software that can use it. Such communication was originally accomplished by having a remote network of machines relay information back to a central hub for analysis, which would then be rerouted into a system like a personal computer.
Reliability-centered maintenance (RCM) is a concept of maintenance planning to ensure that systems continue to do what their user require in their present operating context. Successful implementation of RCM will lead to increase in cost effectiveness, reliability, machine uptime, and a greater understanding of the level of risk that the organization is managing.
A smart transducer is an analog or digital transducer, actuator or sensor combined with a processing unit and a communication interface.
Network Centric Product Support (NCPS) is an early application of an Internet of Things (IoT) computer architecture developed to leverage new information technologies and global networks to assist in managing maintenance, support and supply chain of complex products made up of one or more complex systems, such as in a mobile aircraft fleet or fixed location assets such as in building systems. This is accomplished by establishing digital threads connecting the physical deployed subsystem with its design Digital Twins virtual model by embedding intelligence through networked micro-web servers that also function as a computer workstation within each subsystem component (i.e. Engine control unit on an aircraft) or other controller and enabling 2-way communications using existing Internet technologies and communications networks - thus allowing for the extension of a product lifecycle management (PLM) system into a mobile, deployed product at the subsystem level in real time. NCPS can be considered to be the support flip side of Network-centric warfare, as this approach goes beyond traditional logistics and aftermarket support functions by taking a complex adaptive system management approach and integrating field maintenance and logistics in a unified factory and field environment. Its evolution began out of insights gained by CDR Dave Loda (USNR) from Network Centric Warfare-based fleet battle experimentation at the US Naval Warfare Development Command (NWDC) in the late 1990s, who later lead commercial research efforts of NCPS in aviation at United Technologies Corporation. Interaction with the MIT Auto-ID Labs, EPCglobal, the Air Transport Association of America ATA Spec 100/iSpec 2200 and other consortium pioneering the emerging machine to machine Internet of Things (IoT) architecture contributed to the evolution of NCPS.
STEP-NC is a machine tool control language that extends the ISO 10303 STEP standards with the machining model in ISO 14649, adding geometric dimension and tolerance data for inspection, and the STEP PDM model for integration into the wider enterprise. The combined result has been standardized as ISO 10303-238.
Embedded intelligence is characterized as the ability of a product, process, or service to reflect on its own operational performance, usage load, or environment. The motivation for this may be to enhance the performance, lifetime, or quality of the product. This self-reflection might be facilitated by information collected via embedded sensors, and processed locally or communicated remotely for processing.
Industrial and production engineering (IPE) is an interdisciplinary engineering discipline that includes manufacturing technology, engineering sciences, management science, and optimization of complex processes, systems, or organizations. It is concerned with the understanding and application of engineering procedures in manufacturing processes and production methods. Industrial engineering dates back all the way to the industrial revolution, initiated in 1700s by Sir Adam Smith, Henry Ford, Eli Whitney, Frank Gilbreth and Lilian Gilbreth, Henry Gantt, F.W. Taylor, etc. After the 1970s, industrial and production engineering developed worldwide and started to widely use automation and robotics. Industrial and production engineering includes three areas: Mechanical engineering, industrial engineering, and management science.
The Industrial Internet Consortium rebranded as the Industry IoT Consortium in August 2021. The Industry IoT Consortium is a program of the Object Management Group (OMG).
Robotic process automation (RPA) is a form of business process automation that is based on software robots (bots) or artificial intelligence (AI) agents. It is sometimes referred to as software robotics.
A digital twin is a digital model of an intended or actual real-world physical product, system, or process that serves as the effectively indistinguishable digital counterpart of it for practical purposes, such as simulation, integration, testing, monitoring, and maintenance. The digital twin has been intended from its initial introduction to be the underlying premise for Product Lifecycle Management and exists throughout the entire lifecycle of the physical entity it represents. Since information is granular, the digital twin representation is determined by the value-based use cases it is created to implement. The digital twin can and does often exist before there is a physical entity. The use of a digital twin in the creation phase allows the intended entity's entire lifecycle to be modeled and simulated. A digital twin of an existing entity may be used in real-time and regularly synchronized with the corresponding physical system.
Asif Jamal mansooriIndustrial big data refers to a large amount of diversified time series generated at a high speed by industrial equipment, known as the Internet of things. The term emerged in 2012 along with the concept of "Industry 4.0”, and refers to big data”, popular in information technology marketing, in that data created by industrial equipment might hold more potential business value. Industrial big data takes advantage of industrial Internet technology. It uses raw data to support management decision making, so to reduce costs in maintenance and improve customer service. Please see intelligent maintenance system for more reference.
Cyber manufacturing is a concept derived from cyber-physical systems (CPS) that refers to a modern manufacturing system that offers an information-transparent environment to facilitate asset management, provide reconfigurability, and maintain productivity. Compared with conventional experience-based management systems, cyber manufacturing provides an evidence-based environment to keep equipment users aware of networked asset status, and transfer raw data into possible risks and actionable information. Driving technologies include design of cyber-physical systems, combination of engineering domain knowledge and computer sciences, as well as information technologies. Among them, mobile applications for manufacturing is an area of specific interest to industries and academia.
Industrial artificial intelligence, or industrial AI, usually refers to the application of artificial intelligence to industry. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis and insight discovery.
The industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers' industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency as well as other economic benefits. The IIoT is an evolution of a distributed control system (DCS) that allows for a higher degree of automation by using cloud computing to refine and optimize the process controls.
Data center management is the collection of tasks performed by those responsible for managing ongoing operation of a data center. This includes Business service management and planning for the future.
References
- ↑ Moore, W. J.; A. G. Starr (August 2006). "An intelligent maintenance system for continuous cost-based prioritisation of maintenance activities" (PDF). Computers in Industry. 57 (6): 596. doi:10.1016/j.compind.2006.02.008. hdl: 2299/2247 .
- ↑ Baldwin, Robert C (2001). "Enabling an e-Maintenance infrastructure". Maintenance Technology. 12.