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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  > In Press, Accepted Manuscript
X. Cao, K. Peng, and R. Jiao, “Multi-phase degradation modeling based on uncertain random process for remaining useful life prediction under triple uncertainties,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–15, Jan. 2025.
Citation: X. Cao, K. Peng, and R. Jiao, “Multi-phase degradation modeling based on uncertain random process for remaining useful life prediction under triple uncertainties,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–15, Jan. 2025.
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Multi-Phase Degradation Modeling Based on Uncertain Random Process for Remaining Useful Life Prediction Under Triple Uncertainties

Funds:  This work was supported by the National Key Research and Development Program of China (2021YFB3301200) and the National Natural Science Foundation of China (NSFC) (U21A20483, 62373040, 62203042)
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    Abstract
  • Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure, degradations of some equipment are characterized by multi-phase and jumps. Meanwhile, equipment is subject to inherent fluctuations, limited data and imperfect measurements resulting in aleatory, epistemic and measurement uncertainties of the degradation process. This paper proposes a degradation model and remaining useful life (RUL) prediction method under triple uncertainties for a category of complex equipment with multi-phase degradation and jumps. First, a multi-phase degradation model with random jumps and measurement errors is constructed based on uncertain random processes. Afterward, the analytic expression of RUL prediction considering the heterogeneity is derived by modeling the uncertainty of degradation states at change points under the concept of first hitting time. A stochastic uncertain approach is utilized for the proposed multi-phase degradation model to identify model parameters based on historical data. Furthermore, the implied degradation features are adaptively updated in online stage using similarity-based weighted stochastic uncertain maximum likelihood estimation and Kalman filtering. Finally, the effectiveness of the method is verified by simulation example and practical case.

     

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