Leveraging Historical Breakdown Data for Enhanced Predictive and Prescriptive Maintenance Insights

Amit Saxena

doi:10.14513/actatechjaur.00809

Authors

Amit Saxena College of Science and Technology, Bellevue University 1000 Galvin Road South, NE 68005, Bellevue, USA

DOI:

https://doi.org/10.14513/actatechjaur.00809

Keywords:

Predictive Maintenance, Prescriptive Maintenance, Historical breakdown data, Machine Learning, Failure Prediction, Industrial Maintenance

Abstract

The application of predictive and prescriptive maintenance procedures in industries is revolutionizing mainstream manufacturing by cutting down on time loss and waste of resources. Reactive maintenance and preventive strategies are some of the traditional maintenance management techniques that tend to cause inefficiency in the systems, high operational costs and some failures. This paper uses data from breakdown analysis in the development of predictive maintenance models and prescriptive decision systems. A methodology is used that incorporates predictive analytics based on individual machine learning with the knowledge of the failure patterns. The analysis of historical breakdown records allows predictive models to achieve higher accuracy in forecasting potential failures by identifying key failure trends. The prescriptive maintenance program provides information regarding the best course of action to be taken, minimizing operational disruptions and downtimes. As means of testing the efficiency of the proposed concept, experiments were conducted on real-world industrial datasets. The implications of this are lower number of unplanned maintenance interventions, increased efficiency, and reduced costs. This paper adds to the literature on predictive and prescriptive maintenance as it highlights how historical breakdown information can enhance the predictive analysis while giving suggestions concerning industrial maintenance management. Further research on deep learning algorithms and real-time integration of the sensors have potential to improve maintenance processes.

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