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Reliability analysis of products based on proportional hazard model with degradation trend and environmental factor

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  • Zheng, Huiling
  • Kong, Xuefeng
  • Xu, Houbao
  • Yang, Jun

Abstract

Degradation data and lifetime data have been broadly used for assessing product and system reliability. To utilize the two kinds of data simultaneously, the proportional hazard (PH) model with degradation data as a covariate is proposed for reliability analysis. However, most existing works focus on modeling the PH model with degradation state as a covariate, while the degradation trend is ignored, which makes the PH model unable to carry out reliability prediction directly. Confronted with that, a new PH model with the degradation trend and environmental factor as covariates is developed in this paper. The Wiener process is firstly applied to depict the degradation trend, then the degradation trend and temperature are used as covariates to establish the PH model, and a closed-form of the reliability is derived by the Taylor approximation. Based on the degradation data under actual ambient conditions, the real-time updated reliability prediction is provided to guide the health management of products and systems. The simulation study validates that the proposed model outperforms two existing models in terms of Mean Square Error (MSE). Finally, a real-world example of MOSFET is presented to demonstrate the implementation of the proposed method.

Suggested Citation

  • Zheng, Huiling & Kong, Xuefeng & Xu, Houbao & Yang, Jun, 2021. "Reliability analysis of products based on proportional hazard model with degradation trend and environmental factor," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004750
    DOI: 10.1016/j.ress.2021.107964
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    References listed on IDEAS

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    3. Zheng, Huiling & Yang, Jun & Xu, Houbao & Zhao, Yu, 2023. "Reliability acceptance sampling plan for degraded products subject to Wiener process with unit heterogeneity," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Wang, Yueyao & Lee, I-Chen & Hong, Yili & Deng, Xinwei, 2022. "Building degradation index with variable selection for multivariate sensory data," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    5. Luo, Yi & Zhao, Xiujie & Liu, Bin & He, Shuguang, 2024. "Condition-based maintenance policy for systems under dynamic environment," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    6. Song, Wanqing & Duan, Shouwu & Zio, Enrico & Kudreyko, Aleksey, 2022. "Multifractional and long-range dependent characteristics for remaining useful life prediction of cracking gas compressor," Reliability Engineering and System Safety, Elsevier, vol. 225(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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