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http://dx.doi.org/10.11627/jksie.2022.45.3.018

Cost-optimal Preventive Maintenance based on Remaining Useful Life Prediction and Minimum-repair Block Replacement Models  

Choo, Young-Suk (Graduate School of Technology and Innovation Management, Hanyang University)
Shin, Seung-Jun (School of Interdisciplinary Industrial Studies, Hanyang University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.45, no.3, 2022 , pp. 18-30 More about this Journal
Abstract
Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.
Keywords
Remaining Useful Life; Predictive Maintenance; Preventive Maintenance; Weibull Distribution; Minimum-Repair Block Replacement;
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Times Cited By KSCI : 1  (Citation Analysis)
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