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Time series chain graph for modeling reliability covariates in degradation process

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  • Xu, Huyang
  • Fard, Nasser
  • Fang, Yuanchen

Abstract

In product health management, degradation modeling methods have been recognized as essential and effective for the lifetime and remaining useful life (RUL) estimations. In many applications, covariate-related data provided by product users can be regarded as fragments of life-cycle records. For a particular fragment, it is possible to suggest several possible degradation conditions simultaneously. These degradation conditions may lead to different results of the RUL estimation. One way to solve such a problem is to increase the life-cycle degradation model's screening capacity of degradation conditions. In this paper, time series chain graph (TSCG), which could effectively determine the possible degradation conditions by modeling the dependencies between time-varying risk factors and performance measurements, is proposed. The procedures of model construction based on observed time series and the use of the proposed model for RUL prediction are given. Based on the inherent complexity of the TSCG structure, it is possible to distinguish the degradation conditions better so that RUL's identification is more reliable. Finally, the validity of the proposed model is illustrated by a turbofan engine degradation case study, which consists of the time series for engine operation and degradation process.

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  • Xu, Huyang & Fard, Nasser & Fang, Yuanchen, 2020. "Time series chain graph for modeling reliability covariates in degradation process," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020307080
    DOI: 10.1016/j.ress.2020.107207
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    References listed on IDEAS

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    Cited by:

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    2. Nguyen, Khanh T.P. & Medjaher, Kamal & Gogu, Christian, 2022. "Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Salem, Marwa Belhaj & Fouladirad, Mitra & Deloux, Estelle, 2022. "Variance Gamma process as degradation model for prognosis and imperfect maintenance of centrifugal pumps," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    4. Zhuang, Liangliang & Xu, Ancha & Wang, Xiao-Lin, 2023. "A prognostic driven predictive maintenance framework based on Bayesian deep learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

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