A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
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.
shu

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)
More Information
    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.

     

    • FullText(HTML)
  • loading
    • References(40)
  • [1]
    N. Lu, C. Chen, B. Jiang, and Y. Xing, “Latest progress on maintenance strategy of complex system: From condition-based maintenance to predictive maintenance,” Acta Autom. Sinica, vol. 47, no. 1, pp. 1–17, Jan. 2021.
    [2]
    E. Zio, “Prognostics and health management (PHM): Where are we and where do we (need to) go in theory and practice,” Rel. Eng. Syst. Saf., vol. 218, p. 108119, Feb. 2022. doi: 10.1016/j.ress.2021.108119
    [3]
    J. Hu, Q. Sun, Z. Ye, and Q. Zhou, “Joint modeling of degradation and lifetime data for RUL prediction of deteriorating products,” IEEE Trans. Ind. Inform., vol. 17, no. 7, pp. 4521–4531, Jul. 2020.
    [4]
    X. Si, W. Wang, C. Hu, and D. Zhou, “Remaining useful life estimation—A review on the statistical data driven approaches,” Eur. J. Oper. Res., vol. 213, no. 1, pp. 1–14, Aug. 2011. doi: 10.1016/j.ejor.2010.11.018
    [5]
    Y. Lei, N. Li, L. Guo, N. Li, T. Yan, and J. Lin, “Machinery health prognostics: A systematic review from data acquisition to RUL prediction,” Mech. Syst. Signal Process, vol. 104, pp. 799–834, May 2018. doi: 10.1016/j.ymssp.2017.11.016
    [6]
    Y. Lin, Y. Li, and E. Zio, “Integrating random shocks into multistate physics models of degradation processes for component reliability assessment,” IEEE Trans. Rel., vol. 64, pp. 154–166, Mar. 2015. doi: 10.1109/TR.2014.2354874
    [7]
    H. Meng and Y. Li, “A review on prognostics and health management (PHM) methods of lithium-ion batteries,” Renew. Sustain. Energy Rev., vol. 116, p. 109405, Dec. 2019. doi: 10.1016/j.rser.2019.109405
    [8]
    K. Zhong, M. Han, and B. Han, “Data-driven based fault prognosis for industrial systems: A concise overview,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 19–34, Mar. 2020.
    [9]
    R. Jiao, K. Peng, and J. Dong, “Remaining useful life prediction for a roller in a hot strip mill based on deep recurrent neural networks,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1345–1354, Jul. 2021. doi: 10.1109/JAS.2021.1004051
    [10]
    H. Sun, D. Cao, Z. Zhao, and X. Kang, “A hybrid approach to cutting tool remaining useful life prediction based on the Wiener process,” IEEE Trans. Rel., vol. 67, no. 3, pp. 1–10, Sep. 2018. doi: 10.1109/TR.2018.2865055
    [11]
    Z. Zhang, C. Hu, X. Si, J. Zhang, and J. Zheng, “Stochastic degradation process modeling and remaining useful life estimation with flexible random-effects,” J. Franklin Inst., vol. 354, no. 6, pp. 2477–2499, Apr. 2017. doi: 10.1016/j.jfranklin.2016.06.039
    [12]
    J. Lin, Z. Lin, G. Liao, and H. Yin, “A novel product remaining useful life prediction approach considering fault effects,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 11, pp. 1762–1773, Nov. 2021. doi: 10.1109/JAS.2021.1004168
    [13]
    H. Wang, H. Liao, and X. Ma, “Stochastic multi-phase modeling and health assessment for systems based on degradation branching processes,” Rel. Eng. Syst. Saf., vol. 222, p. 108412, Jun. 2022. doi: 10.1016/j.ress.2022.108412
    [14]
    D. Kong, N. Balakrishnan, and L. Cui, “Two-phase degradation process model with abrupt jump at change point governed by wiener process,” IEEE Trans. Rel., vol. 66, no. 4, pp. 1345–1360, Jun. 2017. doi: 10.1109/TR.2017.2711621
    [15]
    H. Gao, L. Cui, and Q. Dong, “Reliability modeling for a two-phase degradation system with a change point based on a Wiener process,” Rel. Eng. Syst. Saf., vol. 193, p. 106601, Jan. 2020. doi: 10.1016/j.ress.2019.106601
    [16]
    Y. Wen, J. Wu, D. Das, and T. Tseng, “Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity,” Rel. Eng. Sys. Saf., vol. 176, pp. 113–124, Aug. 2018. doi: 10.1016/j.ress.2018.04.005
    [17]
    G. Liao, H. Yin, M. Chen, and Z. Lin, “Remaining useful life prediction for multi-phase deteriorating process based on Wiener process,” Rel. Eng. Sys. Saf., vol. 207, p. 107361, Mar. 2021. doi: 10.1016/j.ress.2020.107361
    [18]
    P. Wang, Y. Tang, S. Bae, and A. Xu, “Bayesian approach for two-Phase degradation data based on change-point Wiener process with measurement errors,” IEEE Trans. Rel., vol. 67, no. 2, pp. 688–670, Jun. 2018. doi: 10.1109/TR.2017.2785978
    [19]
    Q. Guan, X. Wei, W. Bai, and L. Jia, “Two-stage degradation modeling for remaining useful life prediction based on the Wiener process with measurement errors,” Qual. Rel. Eng. Int., vol. 38, no. 7, pp. 3485–3512, May 2022. doi: 10.1002/qre.3147
    [20]
    W. Lin and Y. Chai, “Remaining useful life prediction for nonlinear two-phase degradation process with measurement errors and imperfect prior information,” Meas. Sci. Tech., vol. 34, no. 5, p. 55018, Feb. 2023.
    [21]
    A. D. Kiureghian, and O. Ditlevsen, “Aleatory or epistemic? Does it matter?” Struct Saf., vol. 31, no. 2, pp. 105–112, Mar. 2009. doi: 10.1016/j.strusafe.2008.06.020
    [22]
    G. A. Whitmore, “Estimating degradation by a Wiener diffusion process subject to measurement error,” Lifetime Data Anal., vol. 1, no. 3, pp. 307–319, May 1995. doi: 10.1007/BF00985762
    [23]
    R. Ge, Q. Zhai, H. Wang, and Y. Huang, “Wiener degradation models with scale-mixture normal distributed measurement errors for RUL prediction,” Mech. Syst. Signal Process, vol. 173, p. 109029, Jul. 2022. doi: 10.1016/j.ymssp.2022.109029
    [24]
    J. Liu, Z. Yu, H. Zuo, R. Fu, and X. Feng, “Multi-stage residual life prediction of aero-engine based on real-time clustering and combined prediction model,” Rel. Eng. Syst. Saf., vol. 225, p. 108624, Sep. 2022. doi: 10.1016/j.ress.2022.108624
    [25]
    J. Zhang, C. Hu, X. He, X. Si, Y. Liu, and D. Zhou, “A novel lifetime estimation method for two-phase degrading systems,” IEEE Trans. Rel., vol. 68, no. 2, pp. 689–709, Jun. 2019. doi: 10.1109/TR.2018.2829844
    [26]
    C. Hu, Y. Xing, D. Du, X. Si, and J. Zhang, “Remaining useful life estimation for two-phase nonlinear degradation processes,” Rel. Eng. Syst. Saf., vol. 230, p. 108945, Feb. 2023. doi: 10.1016/j.ress.2022.108945
    [27]
    J. Ma, L. Cai, G. Liao, H. Yin, X. Si, and P. Zhang, “A multi-phase Wiener process-based degradation model with imperfect maintenance activities,” Rel. Eng. Syst. Saf., vol. 232, p. 109075, Apr. 2023. doi: 10.1016/j.ress.2022.109075
    [28]
    Z. Sheng, Q. Hu, J. Liu, and D. Yu, “Residual life prediction for complex systems with multi-phase degradation by ARMA-filtered hidden Markov model,” Qual. Tech. Quant. M., vol. 16, no. 1, pp. 19–35, Jun. 2019. doi: 10.1080/16843703.2017.1335496
    [29]
    B. Liu, Uncertainty Theory, 2nd ed. Berlin, Germary: Springer-Verlag, 2007.
    [30]
    X. Li, J. Wu, L. Liu, M. Wen, and R. Kang, “Modeling accelerated degradation data based on the uncertain process,” IEEE Trans. Fuzzy Syst., vol. 2, pp. 1532–1542, Aug. 2019.
    [31]
    S. Zhang, R. Kang, and Y. Lin, “Remaining useful life prediction for degradation with recovery phenomenon based on uncertain process,” Rel. Eng. Syst. Saf., vol. 208, p. 107440, Apr. 2021. doi: 10.1016/j.ress.2021.107440
    [32]
    L. Zhang, J. Zhang, L. You, and S. Zhou, “Reliability analysis of structures based on a probability-uncertainty hybrid model,” Qual. Reliab. Engng. Int., vol. 35, pp. 263–279, Feb. 2019. doi: 10.1002/qre.2396
    [33]
    L. Hu, R. Kang, X. Pan, and D. Zuo, “Uncertainty expression and propagation in the risk assessment of uncertain random system,” IEEE Syst. J., vol. 15, no. 2, pp. 1604–1615, Jun. 2023.
    [34]
    Y. Wang, Q. Liu, W. Lu, and Y. Peng, “A general time-varying Wiener process for degradation modeling and RUL estimation under three-source variability,” Rel. Eng. Syst. Saf., vol. 232, p. 109041, Apr. 2023. doi: 10.1016/j.ress.2022.109041
    [35]
    X. Cao and K. Peng, “Multi-phase degradation modeling and remaining useful life prediction considering aleatory and epistemic uncertainty,” IEEE Sensors J., vol. 23, no. 22, pp. 27757–27770, Nov. 2023. doi: 10.1109/JSEN.2023.3323476
    [36]
    J. Gao and K. Yao, “Some concepts and theorems of uncertain random process,” Int. J. Intell. Syst., vol. 30, no. 1, pp. 52–65, Jan. 2015. doi: 10.1002/int.21681
    [37]
    J. M. Pan and J. Chen, “Application of modified information criterion to multiple change point problems,” J. Multivariate Anal., vol. 97, no. 10, pp. 2221–2241, Nov. 2006. doi: 10.1016/j.jmva.2006.05.009
    [38]
    W. Lio and B. Liu, “Uncertain maximum likelihood estimation with application to uncertain regression analysis,” Soft Comput., vol. 24, no. 4, pp. 9351–9360, Apr. 2020.
    [39]
    B. Wang, Y. Lei, N. Li, and N. Li, “A hybrid prognostics approach for estimating remaining useful life of rolling element bearings,” IEEE Trans. Reliab., vol. 69, no. 1, pp. 401–412, Mar. 2020. doi: 10.1109/TR.2018.2882682
    [40]
    A. Molini, P. Talkner, G. G. Katul, and A. Porporato, “First passage time statistics of Brownian motion with purely time dependent drift and diffusion,” Physica A: Statist. Mech. Appl., vol. 390, no. 11, pp. 1841–1852, Jun. 2011. doi: 10.1016/j.physa.2011.01.024
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(10)  / Tables(6)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (6) PDF downloads(2) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return