A Prognosis-Centered Intelligent Maintenance Optimization Framework Under Uncertain Failure Threshold

• Published in: IEEE transactions on reliability Vol. 73; no. 1; pp. 115 - 130
• Main Authors: Yang, Li, Chen, Yi, Ma, Xiaobing, Qiu, Qingan, Peng, Rui
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Asset management; Condition monitoring; Costs; Decision making; Degradation; Downtime; Equipment failure; Harnesses; High speed rail; Inspection; inspection optimization; intelligent maintenance; Intelligent systems; Lifetime estimation; lifetime prognosis; Maintenance; Maintenance engineering; Nuclear devices; Nuclear safety; Optimization; Prognosis; Prognostics and health management; Reliability; Reliability engineering; reliability evaluation; Stochastic processes; Wind power
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2023.3273082

Condition-based maintenance (CBM), as a key component of asset health management, is crucial to enhance the operational safety and availability of diverse mechatronic systems, such as railway vehicles, wind power equipment, nuclear devices, etc. A common phenomenon observed in CBM is the existence of dispersibility regarding degradation-induced failure threshold, which affects the precision of maintenance decisions. This article addresses such challenges by scheduling a prognosis-centered intelligent CBM policy, which harnesses dynamic lifetime information to support both scheduled and opportunistic maintenance decision-making. The degradation is characterized by a generalized-form stochastic process, and the lifetime distribution is assessed through the fusion of multiple uncertainties. A dynamic reliability criterion is set to determine whether and when to postpone maintenance, whose interval is controlled by the remaining lifetime as well as an optimizable safety coefficient. The postponement interval, in turn, enables the planning of opportunistic maintenance to mitigate system downtime. The operational cost rate is minimized through the joint optimization of the inspection interval, conditional reliability threshold, and safety coefficient. The superiorities of the proposed policy over some conventional/heuristic maintenance policies are demonstrated by a case study on filed maintenance planning of high-speed train bearing.