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Two-phase degradation data analysis with change-point detection based on Gaussian process degradation model

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  • Chen, Zhen
  • Li, Yaping
  • Zhou, Di
  • Xia, Tangbin
  • Pan, Ershun

Abstract

Degradation paths of the products exhibiting two-phase patterns are commonly seen in practice due to the changeable internal mechanisms and external environments. In this paper, we propose a two-phase Gaussian process (TPGP) degradation model with a change-point, which comprises the Wiener process-based change-point models as special cases, to describe the degradation paths with two-phase patterns. The change-point is used to represent the transition of degradation characteristics. The degradation rates and variations in the two phases are assumed to be different. Therefore, both monotonically increasing and decreasing or nonmonotonic dispersion trends and complicated auto-correlations in the degradation measurements can be captured by TPGP. Joint methods of the parameter estimation and change-point detection is developed for two different engineering scenarios. The distributions of the first passage time and the remaining useful life are derived in closed-form to promote the mathematical trackability and the applicability of the TPGP model. A comprehensive simulation study shows the effectiveness and validity of the proposed model and method. Finally, we use two real applications to demonstrate the proposed models and methods.

Suggested Citation

  • Chen, Zhen & Li, Yaping & Zhou, Di & Xia, Tangbin & Pan, Ershun, 2021. "Two-phase degradation data analysis with change-point detection based on Gaussian process degradation model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
  • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004324
    DOI: 10.1016/j.ress.2021.107916
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    References listed on IDEAS

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    1. Gao, Hongda & Cui, Lirong & Dong, Qinglai, 2020. "Reliability modeling for a two-phase degradation system with a change point based on a Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
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    Cited by:

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    6. Lyu, Dongzhen & Niu, Guangxing & Liu, Enhui & Zhang, Bin & Chen, Gang & Yang, Tao & Zio, Enrico, 2022. "Time space modelling for fault diagnosis and prognosis with uncertainty management: A general theoretical formulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Ding, Wanmeng & Li, Jimeng & Mao, Weilin & Meng, Zong & Shen, Zhongjie, 2023. "Rolling bearing remaining useful life prediction based on dilated causal convolutional DenseNet and an exponential model," Reliability Engineering and System Safety, Elsevier, vol. 232(C).

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