Bi-level corrected residual life-based maintenance for deteriorating systems under competing risks

The accurate prediction of residual life in deteriorating systems is a challenging task due to uncertainties and idealized assumptions during data acquisition and model construction. To address this challenge, the paper proposes a bi-level maintenance model that incorporates a corrected residual life (CRL) approach for a deteriorating system under competing risks. Specifically, the degradation failure is modeled using a Gamma process, while the competing sudden failure is described by a proportional hazards (PH) model. To reduce inconsistencies in residual life prediction, a CRL model is presented and continuously updated with a dynamic corrective factor at each monitoring epoch. Based on the updated CRL, a bi-level maintenance model is developed, which considers both system availability and maintenance cost objectives, enabling dynamic monitoring of the system's degradation state and residual life. The optimal decision variables in the maintenance model are determined through a multi-objective optimization algorithm formulated within a semi-Markov decision process (SMDP) framework. The unique aspect of this work lies in the consideration of prediction errors in residual life and the incorporation of multi-attribute optimization for maintenance design. The proposed method is validated through a case study on light-emitting diodes, confirming its effectiveness in improving residual life prediction and optimizing maintenance decisions.