Predictive Maintenance and Health Monitoring
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I4P101Keywords:
Predictive Maintenance, Health Monitoring, Machine Learning, Vibration Analysis, Remaining Useful Life, Industrial IOT, Fault Detection, Condition MonitoringAbstract
Predictive maintenance (PdM) is a new trend in the modern industrial work as it allows companies to decrease downtimes, lengthen equipment lives and systems and apply more aggressive maintenance planning. Health monitoring of industrial machines is nowadays transformed due to the implementation of advanced sensors, Internet of Things (IoT), and machine-learning algorithms with the dawn of Industry 4.0. The following paper is the comprehensive review of predictive maintenance and health monitoring of industrial equipment and methodology. The paper highlights the three main areas that focus on data collection, feature identification, health conditions evaluation, fault anticipation, and maintenance arrangements. Vibration analysis, thermal imaging, acoustic monitoring, sensor fusion, and many other techniques are discussed to increase the accuracy of fault detection. There are machine learning models such as Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks (ANN), and deep learning systems that are used in anomaly detection and prognosis. The paper is also presented with a detailed case study of implementing PdM within a manufacturing environment in which it was shown that the downtime rates caused by actual reasons were reduced dramatically as well as the expense of maintenance. Mean Time Between Failures (MTBF), Remaining Useful Life (RUL) and predictive accuracy, performance metrics are evaluated to measure the adequacy of the proposed methodology. Additionally, the challenges and future trends are discussed like real-time monitoring, edge computing, and digital twins. This paper is a step-by-step guide on how researchers and industry practitioners can integrate predictive maintenance and health monitoring to the modern industrial setting
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