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

Author

Listed:
  • Liao, Jing
  • Peng, Tao
  • Xu, Yansong
  • Gui, Gui
  • Yang, Chao
  • Yang, Chunhua
  • Gui, Weihua

Abstract

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.

Suggested Citation

  • Liao, Jing & Peng, Tao & Xu, Yansong & Gui, Gui & Yang, Chao & Yang, Chunhua & Gui, Weihua, 2024. "Task-orientated probabilistic damage model with interdependent degradation behaviors for RUL prediction of traction converter systems," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:reensy:v:250:y:2024:i:c:s0951832024003740
    DOI: 10.1016/j.ress.2024.110302
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    References listed on IDEAS

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