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Predictive maintenance evaluates the condition of equipment by performing periodic (offline) or continuous (online) equipment condition monitoring.The ultimate goal of the approach is to perform maintenance at a scheduled point in time when the maintenance activity is most cost-effective and before the equipment loses performance within a threshold.
It is a major component of predictive maintenance. The use of condition monitoring allows maintenance to be scheduled, or other actions to be taken to prevent consequential damages and avoid its consequences.
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.
An intelligent maintenance system is a system that uses data analysis and decision support tools to predict and prevent the potential failure of machines. The recent advancement in information technology, computers, and electronics have facilitated the design and implementation of such systems.
In the present era of Industry 4.0, the industries are implementing a predictive maintenance strategy for their mechanical systems. The FMECA is widely used for the failure mode identification and prioritization of mechanical systems and their subsystems for predictive maintenance. [18]
Total productive maintenance (TPM) was developed by Seiichi Nakajima in Japan between 1950 and 1970. This experience led to the recognition that a leadership mindset engaging front line teams in small group improvement activity is an essential element of effective operation.
Understanding the "as is" state of an asset and its major components is an essential element in preventive maintenance as used in maintenance, repair, and overhaul and enterprise asset management systems. Complex assets such as aircraft, ships, industrial machinery etc. depend on many different components being serviceable.
Predictive Failure Analysis was originally used as term for a proprietary IBM technology for monitoring the likelihood of hard disk drives to fail, although the term is now used generically for a variety of technologies for judging the imminent failure of CPU's, memory and I/O devices. [1] See also first failure data capture.
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