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Hybrid remaining useful life prediction method. A case study on railway D-cables

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  • Zang, Yu
  • Shangguan, Wei
  • Cai, Baigen
  • Wang, Huasheng
  • Pecht, Michael. G.

Abstract

This paper develops a hybrid remaining useful life (RUL) prediction method and explores the feasibility for complex system equipment, using one of transmission equipment D-cables in high-speed railways as an example. RUL prediction is a promising way to reduce high maintenance costs for high-speed railways. However, there is no sufficient actual life-cycle data due to the lack of sensors, and no mature physics-of-failure model of the equipment in high-speed railways, which make it difficult to predict RUL. To solving this problem, firstly the failure modes, mechanisms, and effects of the D-cables are first analyzed, and accelerated life tests are run under different thermal stresses in Ansys to obtain the life-cycle data. Based on the life-cycle data, particle filtering (PF) method predicts the RUL based on Paris-Law model, meanwhile feedforward neural network (FNN) predicts the RUL under the same thermal stress with PF method, finally a hybrid RUL prediction method that combines model-based and data-driven methods is developed. The results are verified using simulation.

Suggested Citation

  • Zang, Yu & Shangguan, Wei & Cai, Baigen & Wang, Huasheng & Pecht, Michael. G., 2021. "Hybrid remaining useful life prediction method. A case study on railway D-cables," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021002775
    DOI: 10.1016/j.ress.2021.107746
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