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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.
The use of condition monitoring allows maintenance to be scheduled, or other actions to be taken to prevent consequential damages and avoid its consequences. Condition monitoring has a unique benefit in that conditions that would shorten normal lifespan can be addressed before they develop into a major failure.
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.
FMECA is the most popular tool for failure and criticality analysis of systems for performance enhancement. In the present era of Industry 4.0, the industries are implementing a predictive maintenance strategy for their mechanical systems.
Unlike other BI technologies, predictive analytics is forward-looking, using past events to anticipate the future. [3] Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. Often the unknown event of interest is in the future, but predictive analytics can be applied to ...
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1] AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment.
Reliability-centered maintenance (RCM) is a concept of maintenance planning to ensure that systems continue to do what their users require in their present operating context. [1] 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 ...
Data-driven prognostics usually use pattern recognition and machine learning techniques to detect changes in system states. [3] The classical data-driven methods for nonlinear system prediction include the use of stochastic models such as the autoregressive (AR) model, the threshold AR model, the bilinear model, the projection pursuit, the multivariate adaptive regression splines, and the ...