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Stochastic Multi-phase Modeling and Health Assessment for Systems Based on Degradation Branching Processes

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  • Wang, Han
  • Liao, Haitao
  • Ma, Xiaobing

Abstract

Degradation modeling and health assessment play a critical role in predictive maintenance of engineering systems. In a multi-component system, the degradation of a component may propagate to other components after a random time period, resulting in a multi-phase system-level degradation behavior. Although extensive work has been conducted on system degradation modeling, traditional methods are not adequate to model such degradation and propagation phenomena simultaneously. In this paper, a stochastic multi-phase modeling approach is proposed for such systems based on the degradation branching processes. The statistical properties of general degradation branching processes are analyzed in regards to two common types of system failures. Specially, when each branch of the multi-phase degradation process can be described by a Wiener process, the analytical expressions for system reliability and lifetime distribution are derived after dividing the entire degradation process into multiple phases based on the locations of branching particles. Based on these results, system health assessment can be conducted by incorporating the current degradation state and the importance of each component. Case studies on high-speed train bogie systems and solar panel arrays are provided to demonstrate the effectiveness and practical values of the proposed methods in handling such complex degradation phenomena.

Suggested Citation

  • Wang, Han & Liao, Haitao & Ma, Xiaobing, 2022. "Stochastic Multi-phase Modeling and Health Assessment for Systems Based on Degradation Branching Processes," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000849
    DOI: 10.1016/j.ress.2022.108412
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    References listed on IDEAS

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    1. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Franco, Manuel & Vivo, Juana-Maria & Kundu, Debasis, 2020. "A generalized Freund bivariate model for a two-component load sharing system," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    3. Wang, Yukun & Li, Xiaopeng & Chen, Junyan & Liu, Yiliu, 2022. "A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Luo, Chunling & Shen, Lijuan & Xu, Ancha, 2022. "Modelling and estimation of system reliability under dynamic operating environments and lifetime ordering constraints," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    5. Mitov, Kosto V., 2021. "A critical branching process with immigration in varying environments," Statistics & Probability Letters, Elsevier, vol. 168(C).
    6. 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).
    7. Shi, Yue & Zhu, Weihang & Xiang, Yisha & Feng, Qianmei, 2020. "Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    8. Cao, Yingsai & Liu, Sifeng & Fang, Zhigeng & Dong, Wenjie, 2020. "Modeling ageing effects for multi-state systems with multiple components subject to competing and dependent failure processes," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    9. Changxi Wang & Elsayed A. Elsayed, 2020. "Stochastic modeling of degradation branching processes," IISE Transactions, Taylor & Francis Journals, vol. 53(3), pages 365-374, July.
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    1. Jiahua Qiao & Xia Cai & Meiqi Zhang, 2025. "Modified Information Criterion for Testing Changes in the Inverse Gaussian Degradation Process," Mathematics, MDPI, vol. 13(4), pages 1-16, February.
    2. Wei, Yian & Cheng, Yao & Liao, Haitao & Elsayed, Elsayed A., 2026. "Resilience modeling for an engineered network with multimodal performance under multiple recurrent hazards," Reliability Engineering and System Safety, Elsevier, vol. 266(PA).
    3. Ma, Jie & Cai, Li & Liao, Guobo & Yin, Hongpeng & Si, Xiaosheng & Zhang, Peng, 2023. "A multi-phase Wiener process-based degradation model with imperfect maintenance activities," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    4. Gao, Hongda & Yin, Zicheng & Li, Yan & Qiu, Qingan, 2026. "Hybrid stochastic process-based reliability modeling considering two-phase dependence degradation patterns," Reliability Engineering and System Safety, Elsevier, vol. 265(PA).
    5. 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).
    6. Lin, Shuai & Jia, Limin & Zhang, Hengrun & Zhang, Pengzhu & Xiong, Yan, 2026. "Failure propagation analysis of high-speed train systems from the perspective of multi-layer stochastic flow network," Reliability Engineering and System Safety, Elsevier, vol. 265(PA).
    7. Lin, Wenyi & Chai, Yi & Fan, Linchuan & Zhang, Ke, 2024. "Remaining useful life prediction using nonlinear multi-phase Wiener process and variational Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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