Dynamic period detection and maintenance optimization for the Wiener degradation dependence process systems

doi:10.23919/JSEE.2026.000111

Abstract

Abstract:

The implementation of timely monitoring and preventive maintenance plays a fundamental role to ensure the reliable operation of complex systems. Condition-based maintenance strategy offers an effective means to leverage system remaining life information, enabling the application of targeted measures to reduce maintenance costs and elevate overall operational efficiency. This study delves into a performance degradation system affected by external random shocks, utilizing the Wiener process model to characterize the continuous degradation process. Within this framework, two distinct condition-based monitoring schemes are proposed: one is the real-time condition monitoring and the other is the dynamic periodic monitoring. Through the optimization of maintenance strategies for each scheme based on the long-term average cost, the study aims to optimize the preventive maintenance threshold for system failure. The Monte Carlo simulation algorithm is adopted to solve the optimization problem. Finally, a comprehensive numerical example is provided to validate the efficiency of both the models and the proposed maintenance strategies.

Key words: Wiener process, shock effect, real-time condition monitoring, dynamic periodic monitoring, optimization strategy

Hongda GAO, Shiqi WEI, Jianhui CHEN, Qing’an QIU. Dynamic period detection and maintenance optimization for the Wiener degradation dependence process systems[J]. Journal of Systems Engineering and Electronics, 2026, 37(3): 952-963.

Figures/Tables 8

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