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Volume 8 Issue 11
Nov.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Jingdong Lin, Zheng Lin, Guobo Liao and Hongpeng 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
Citation: Jingdong Lin, Zheng Lin, Guobo Liao and Hongpeng 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

A Novel Product Remaining Useful Life Prediction Approach Considering Fault Effects

doi: 10.1109/JAS.2021.1004168
Funds:  This work was supported by General Program of National Natural Science Foundation of China (61773080), China Central Universities Foundation (2019CDYGZD001), Scientific Reserve Talent Programs of Chongqing University (cqu2018CDHB1B04), and Graduate Research and Innovation Foundation of Chongqing (CYB20065)
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  • In this paper, a novel remaining useful life prediction approach considering fault effects is proposed. The Wiener process is used to construct the degradation process of single performance characteristic with the fault effects. The first passage time based remaining useful life distribution is calculated by assuming fault occurrence moment is a random variable and follows a certain distribution. Expectation maximization algorithm is employed to estimate model parameters, where the fault occurrence moment is considered as a missing data. Finally, a Copula function is used to describe the dependence between the multiple performance characteristics and derive joint remaining useful life (RUL) distribution of product with the fault effects. The effectiveness of the proposed approach is verified by the experiments of turbofan engines.


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  • [1]
    M. Pecht and R. Jaai, “A prognostics and health management roadmap for information and electronics-rich systems,” Microelectron. Rel., vol. 50, no. 3, pp. 317–323, 2010. doi: 10.1016/j.microrel.2010.01.006
    E. Principi, D. Rossetti, S. Squartini, and F. Piazza, “Unsupervised electric motor fault detection by using deep autoencoders,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 441–451, Mar. 2019. doi: 10.1109/JAS.2019.1911393
    A. K. S. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mech. Syst. Signal Process, vol. 20, no. 7, pp. 1483–1510, 2006. doi: 10.1016/j.ymssp.2005.09.012
    R. Li, Y. Huang, and J. Wang, “Long-term traffic volume prediction based on K-means Gaussian interval type-2 fuzzy sets,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1344–1351, Nov. 2019.
    Z. Chen and S. Zheng, “Lifetime distribution based degradation analysis,” IEEE Trans. Rel., vol. 54, no. 1, pp. 3–10, Mar. 2005. doi: 10.1109/TR.2004.837519
    N. Z. Gebraeel, M. A. Lawley, R. Li, and J. K. Ryan, “Residual-life distributions from component degradation signals: A Bayesian approach,” IIE Trans., vol. 37, no. 6, pp. 543–557, 2005. doi: 10.1080/07408170590929018
    S. Gao, M. Zhou, Y. Wang, J. Chen, and H. Yachi, “Dendritic neuron model with effective learning algorithms for classification, approximation and prediction,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 2, pp. 601–614, Feb. 2019. doi: 10.1109/TNNLS.2018.2846646
    T. D. Pham, K. Wardell, A. Eklund, and G Salerud, “Classification of short time series in early Parkinson’s disease with deep learning of fuzzy recurrence plots,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1306–1317, Nov. 2019. doi: 10.1109/JAS.2019.1911774
    F. Ahmadzadeh and J. Lundberg, “Remaining useful life estimation: review,” Int. J. Syst. Assur. Eng. Manage., vol. 5, no. 4, pp. 461–474, 2013.
    N. Z. Gebraeel and M. A. Lawley, “A neural network degradation model for computing and updating residual life distributions,” IEEE Trans Auto. Sci. Eng., vol. 5, no. 1, pp. 154–163, 2008. doi: 10.1109/TASE.2007.910302
    L. Ren, L. Zhao, S. Hong, S. Zhao, H. Wang, and L. Zhang, “Remaining useful life prediction for lithium-ion battery: A deep learning approach,” IEEE Access, vol. 6, pp. 50587–50598, 2018. doi: 10.1109/ACCESS.2018.2858856
    J. Wu, C. Deng, X. Shao, and S. Q. Xie, “A reliability assessment method based on support vector machines for CNC equipment,” Sci. Chi. Ser. E:Tech. Sci, vol. 52, no. 7, pp. 1849–1857, 2009. doi: 10.1007/s11431-009-0208-z
    V. Tran, H. Pham, B. Yang, and T. Nguyen, “Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine,” Mech. Sys. and Sig. Pro., vol. 32, pp. 320–330, 2012. doi: 10.1016/j.ymssp.2012.02.015
    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, 2011. doi: 10.1016/j.ejor.2010.11.018
    S. T. Tseng, J. Tang, and I. H. Ku, “Determination of burn-in parameters and residual life for highly reliable products,” Nav. Res. Logist., vol. 50, no. 1, pp. 1–14, 2003. doi: 10.1002/nav.10042
    Q. Zhai and Z. Ye, “RUL prediction of deteriorating products using an adaptive Wiener process model,” IEEE Trans. Ind. Infor., vol. 13, no. 6, pp. 2911–2921, Dec. 2017. doi: 10.1109/TII.2017.2684821
    Z. Ye and M. Xie, “Stochastic modeling and analysis of degradation for highly reliable products,” Appl. Stoch. Mod. Bus. Ind., vol. 31, no. 8, pp. 16–32, 2015.
    S. Lu, H. Lu, and W. J. Kolarik, “Multivariate performance reliability prediction in real-time,” Rel. Eng. Syst. Saf., vol. 72, no. 1, pp. 39–45, 2001. doi: 10.1016/S0951-8320(00)00102-2
    P. Wang and D. W. Coit, “Reliability prediction based on degradation modeling for systems with multiple degradation measures,” in Proc. Annual Sym. Rel. Maintain., Los Angeles, USA, 2004, pp. 302–307.
    F. Sun, L. Liu, X. Li, and H. Liao, “Stochastic modeling and analysis of multiple nonlinear accelerated degradation processes through information fusion,” Sensors, vol. 16, no. 8, Article No. 1242, 2016.
    Z. Pan and N. Balakrishnan, “Reliability modeling of degradation of products with multiple performance characteristics based on gamma processes,” Rel. Eng. Syst. Saf., vol. 96, no. 8, pp. 949–957, 2011. doi: 10.1016/j.ress.2011.03.014
    X. Wang, B. Guo, and J. Cheng, “Residual life estimation based on bivariate Wiener degradation process with time-scale transformations,” J. Stat. Comput. Simul., vol. 84, no. 3, pp. 545–563, 2014. doi: 10.1080/00949655.2012.719026
    X. Wang, N. Balakrishnan, and B. Guo, “Residual life estimation based on nonlinear-multivariate Wiener process,” J. Stat. Comput. Simul., vol. 85, no. 9, pp. 1742–1764, 2015. doi: 10.1080/00949655.2014.898765
    X. Zhang and A. Wilson, “System reliability and component importance under dependence: A Copula approach,” Technometrics, vol. 59, no. 2, pp. 215–224, 2017. doi: 10.1080/00401706.2016.1142907
    J. K. Sari, M. J. Newby, A. C. Brombacher, and L. C. Tang, “Bivariate constant stress degradation model: LED lighting system reliability estimation with two-stage modeling,” Qual. Rel. Eng. Int., vol. 25, no. 8, pp. 1067–1084, 2009. doi: 10.1002/qre.1022
    Z. Xi, R. Jing, P. Wang, and C. Hu, “A Copula-based sampling method for data-driven prognostics,” Rel. Eng. Syst. Saf., vol. 132, pp. 72–82, 2014. doi: 10.1016/j.ress.2014.06.014
    W. Peng, Y. Li, Y. Yang, S. Zhu, and H. Huang, “Bivariate analysis of incomplete degradation observations based on inverse Gaussian processes and Copulas,” IEEE Trans. Rel., vol. 65, no. 2, pp. 624–639, Jun. 2016. doi: 10.1109/TR.2015.2513038
    Y. Wang and H. Pham, “Modeling the dependent competing risks with multiple degradation processes and random shock using time-varying Copulas,” IEEE Trans. Rel., vol. 61, no. 1, pp. 13–22, Mar. 2012. doi: 10.1109/TR.2011.2170253
    C. C. Tsai, S. T. Tseng, and N. Balakrishnan, “Mis-specification analyses of gamma and Wiener degradation processes,” J. Statist. Plan. Inference, vol. 141, no. 12, pp. 3725–3735, 2011. doi: 10.1016/j.jspi.2011.06.008
    P. Wang, Y. Tang, S. J. Bae, and Y. He, “Bayesian analysis of two-phase degradation data based on change-point wiener process,” Rel. Eng. Syst. Saf., vol. 170, pp. 244–256, 2018. doi: 10.1016/j.ress.2017.09.027
    T. S. Ng, “An Application of the EM algorithm to degradation modeling,” IEEE Trans. Rel., vol. 57, no. 1, pp. 2–13, Mar. 2008. doi: 10.1109/TR.2008.916867
    X. Si, C. Hu, X. Kong, and D. Zhou, “A residual storage life prediction approach for systems with operation state switches,” IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6304–6315, Nov. 2014. doi: 10.1109/TIE.2014.2308135
    L. Cui, J. Huang, and Y. Li, “Degradation models with Wiener diffusion processes under calibrations,” IEEE Trans. Rel., vol. 65, no. 2, pp. 613–623, Jun. 2016. doi: 10.1109/TR.2015.2484075
    R. B. Nelsen, An Introduction to Copulas, New York, USA: Springer, 2006.
    A. Saxena, K. Goebel, D. Simon, and N. Eklund, “Damage propagation modeling for aircraft engine run-to-failure simulation,” in Proc. Int. Conf. Prognostics and Health Management, IEEE, Denver, USA, 2008, pp. 1–9.
    I. K. Lin, “A concordance correlation coefficient to cvaluate reproducibility,” Biometrics, vol. 45, no. 1, pp. 255–268, Mar. 1989. doi: 10.2307/2532051
    G. Babu, P. Zhao, and X. Li, “Deep convolutional neural network based regression approach for estimation of remaining useful life,” in Proc. Int. Conf. Database Syst. advanced Appl., Springer, Cham, 2016, pp. 214–228.


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    • A degradation model based on Wiener process is proposed considering the fault effects
    • Expectation maximization algorithm is employed to estimate model parameters
    • The Copula function is used to describe the dependence of characteristics
    • The joint RUL distribution of product considering the fault effects is derived


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