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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.
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.
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.
Another example is MATLAB and its Predictive Maintenance Toolbox [24] which provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis. This toolbox also includes reference examples for motors, gearboxes ...
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.
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.
Digital twins are transforming construction by creating dynamic digital replicas of physical assets. They support health monitoring, ergonomic risk assessment, and predictive maintenance of structures like bridges and historical buildings. Applications also optimize building energy and carbon performance.
Examples of other algorithms used for deterioration modeling are decision tree, k-NN, random forest, gradient boosting trees, random forest regression, and naive Bayes classifier. In this type model usually, the deterioration is predicted using a set of input variables or predictive features.
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