Task-orientated probabilistic damage model with interdependent degradation behaviors for RUL prediction of traction converter systems

• Published in: Reliability engineering & system safety Vol. 250; p. 110302
• Main Authors: Liao, Jing, Peng, Tao, Xu, Yansong, Gui, Gui, Yang, Chao, Yang, Chunhua, Gui, Weihua
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2024
• Subjects: Damage modeling; Interdependent degradation behaviors; RUL prediction; Task profile; RUL prediction; Task profile; Damage modeling; Interdependent degradation behaviors
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2024.110302

•A new damage model integrating the impact of interdependent degradation behaviors and time-varying operations is proposed.•The RUL is predicted considering unit variability, dependent stochasticity, failure stochasticity, and operating conditions.•Combinations of task profile with different distances, speeds, and passenger rates are processed.•An application of the proposed method to actual case studies of different operating routes of high-speed trains is introduced. Remaining useful life (RUL) prediction is crucial for the safety and reliability of engineering systems with stringent requirements, like high-speed trains. Previous studies on fixed or phased conditions ignore the effects of time-varying operations on survivability. Additionally, the stochastic dependence among units is often neglected. To address the above problems, a task-oriented probabilistic damage model with interdependent degradation behaviors (TPDM-IDB) and an RUL prediction method based on TPDM-IDB are proposed. The model integrates three relations: the fundamental relation from load to damage, the task-oriented relation from task to load, and the interdependence relation. First, the degradation process and variability of the failure unit are tracked based on failure physics, mapping load to damage. Next, a task-oriented thermal loading translation is proposed to dynamically update load under time-varying operations. Then, to address the underestimation of life caused by interdependent degradation behaviors, the degradation factor is adjusted by thermal load redistribution. Finally, considering the failure stochasticity, a decision simulation method based on structural reliability is employed to predict the RUL. The effectiveness of the proposed method is validated through simulation examples and actual case studies. Notably, the method uses system tasks as inputs, which is more suitable for practical engineering applications.