The purpose of this study was to propose useful suggestion by analyzing preventive replacement policy under which there are minor and major failure. Here, major failure is defined as the failure of system which causes the system to stop working, however, the minor failure is defined as the situation in which the system is working but there exists inconvenience for the user to experience the degradation of performance. For this purpose, we formulated an expected cost rate as a function of periodic replacement time and the number of system update cycles. Then, using the probability and differentiation theory, we analyzed the cost rate function to find the optimal points for periodic replacement time and the number of system update cycles. Also, we present a numerical example to show how to apply our model to the real and practical situation in which even under the minor failure, the user of system is not willing to replace or repair the system immediately, instead he/she is willing to defer the repair or replacement until the periodic or preventive replacement time. Optimal preventive replacement timing using two variables, which are periodic replacement time and the number of system update cycles, is provided and the effects of those variables on the cost are analyzed.

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.

This paper deals with two forms of preventive replacement policy with minimal repair at failure. Those are, 1. the replacement policy I based on the cumulative operating time. 2. the replacement policy II based on the number of failures. The basic assumptions are; (1) the cost of minimal repair at failure is increasing with the number of failures since the last replacement, (2) the equipment fails stochastically with time.