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Remaining useful life prediction framework for crack propagation with a case study of railway heavy duty coupler condition monitoring

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  • Wang, Chao
  • Zhu, Tao
  • Yang, Bing
  • Yin, Minxuan
  • Xiao, Shoune
  • Yang, Guangwu

Abstract

A general Remaining useful life (RUL) framework for predicting crack propagation of mechanical system structures based on limited condition monitoring data was proposed. For states of invisible cracks, a method that considers the common characteristics of the object was proposed by using the crack propagation stage as the delay time, combining statistical distribution and hypothesis testing. Then, for the states of visible cracks, methods based on a hypothetical distribution and support vector regression with the Kalman filter considering the current state and individual degradation characteristics were proposed. Finally, taking the condition monitoring data of the coupler body crack state as a case, the RUL prediction from the whole to the individual differences is achieved. The research results indicate that the framework achieves a competitive effect in RUL of internal crack propagation structures of railway wagons, which have important theoretical and practical value for integrity assessment and reliability life prediction.

Suggested Citation

  • Wang, Chao & Zhu, Tao & Yang, Bing & Yin, Minxuan & Xiao, Shoune & Yang, Guangwu, 2023. "Remaining useful life prediction framework for crack propagation with a case study of railway heavy duty coupler condition monitoring," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005300
    DOI: 10.1016/j.ress.2022.108915
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    References listed on IDEAS

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    1. Li, Rui & Verhagen, Wim J.C. & Curran, Richard, 2020. "A systematic methodology for Prognostic and Health Management system architecture definition," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. 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).
    3. Chen, Zhen & Li, Yaping & Xia, Tangbin & Pan, Ershun, 2019. "Hidden Markov model with auto-correlated observations for remaining useful life prediction and optimal maintenance policy," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 123-136.
    4. Jiao, Ruihua & Peng, Kaixiang & Dong, Jie & Zhang, Chuanfang, 2020. "Fault monitoring and remaining useful life prediction framework for multiple fault modes in prognostics," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    5. Li, Zan & Wang, Fengming & Wang, Chengjie & Hu, Qingpei & Yu, Dan, 2021. "Reliability modeling and evaluation of lifetime delayed degradation process with nondestructive testing," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    6. Yan, Tao & Lei, Yaguo & Li, Naipeng & Wang, Biao & Wang, Wenting, 2021. "Degradation modeling and remaining useful life prediction for dependent competing failure processes," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    7. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
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    Cited by:

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    2. Xie, Mingjiang & Wang, Yifei & Zhao, Jianli & Pei, Xianjun & Zhang, Tairui, 2024. "Prediction of pipeline fatigue crack propagation under rockfall impact based on multilayer perceptron," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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