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Volume 9 Issue 5
May  2022

IEEE/CAA Journal of Automatica Sinica

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Z. N. Pang, X. S. Si, C. H. Hu, and  Z. X. Zhang,  “An age-dependent and state-dependent adaptive prognostic approach for hidden nonlinear degrading system,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 907–921, May 2022. doi: 10.1109/JAS.2021.1003859
Citation: Z. N. Pang, X. S. Si, C. H. Hu, and  Z. X. Zhang,  “An age-dependent and state-dependent adaptive prognostic approach for hidden nonlinear degrading system,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 907–921, May 2022. doi: 10.1109/JAS.2021.1003859

An Age-Dependent and State-Dependent Adaptive Prognostic Approach for Hidden Nonlinear Degrading System

doi: 10.1109/JAS.2021.1003859
Funds:  This work was supported by the National Key R&D Program of China (2018YFB1306100), the National Natural Science Foundation of China (61922089, 61833016, 62073336, 61903376, 61773386), the National Science Foundation of Shannxi Province (2020JQ-489, 2020JM-360)
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  • Remaining useful life (RUL) estimation approaches on the basis of the degradation data have been greatly developed, and significant advances have been witnessed. Establishing an applicable degradation model of the system is the foundation and key to accurately estimating its RUL. Most current researches focus on age-dependent degradation models, but it has been found that some degradation processes in engineering are also related to the degradation states themselves. In addition, due to different working conditions and complex environments in engineering, the problems of the unit-to-unit variability in the degradation process of the same batch of systems and actual degradation states cannot be directly observed will affect the estimation accuracy of the RUL. In order to solve the above issues jointly, we develop an age-dependent and state-dependent nonlinear degradation model taking into consideration the unit-to-unit variability and hidden degradation states. Then, the Kalman filter (KF) is utilized to update the hidden degradation states in real time, and the expectation-maximization (EM) algorithm is applied to adaptively estimate the unknown model parameters. Besides, the approximate analytical RUL distribution can be obtained from the concept of the first hitting time. Once the new observation is available, the RUL  distribution can be updated adaptively on the basis of the updated degradation states and model parameters. The effectiveness and accuracy of the proposed approach are shown by a numerical simulation and case studies for Li-ion batteries and rolling element bearings.

     

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    Highlights

    • The influence of the degradation rate change among different units is explicitly considered
    • An age- and state-dependent nonlinear degradation model considering the unit-to-unit variability is proposed
    • The uncertainty of the hidden state from the observations is incorporated into the RUL estimation
    • The approximate analytical RUL distribution is obtained from the concept of the FHT
    • The model parameters and hidden states can be updated by KF and EM algorithm to update the RUL distribution and realize real-time RUL estimation

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