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.

본 논문에서는 시스템 연령(年齡)에 의해 보전 활동의 효과를 설명하는 일반 수리(修理) 개념을 이용한 최적 보전(保全) 정책에 대한 연구를 수행하였다. 본 논문에서는 주기적인 일반 수리와 고장 시 최소 수리가 적용되는 최적 보전 정책을 고려하였다. 따라서 일반 수리에 따른 보전 정책의 비용 함수를 도출하였고 최적 보전 정책을 도출하는 알고리즘을 제시하였고 예제를 통해 알고리즘의 성능을 분석하였다. 이 연구를 통해 시스템을 운영하는데 있어서 어느 수준의