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
Volume 8 Issue 2
Feb.  2021

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  > 2021  >  8(2): 412-422
Chuang Chen, Ningyun Lu, Bin Jiang and Cunsong Wang, "A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 412-422, Feb. 2021. doi: 10.1109/JAS.2021.1003835
Citation: Chuang Chen, Ningyun Lu, Bin Jiang and Cunsong Wang, "A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 412-422, Feb. 2021. doi: 10.1109/JAS.2021.1003835
shu

A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance

doi: 10.1109/JAS.2021.1003835
Funds:  This work was support by Natural Science Foundation of China (61873122)
More Information
    Abstract
  • Remaining useful life (RUL) prediction is an advanced technique for system maintenance scheduling. Most of existing RUL prediction methods are only interested in the precision of RUL estimation; the adverse impact of over-estimated RUL on maintenance scheduling is not of concern. In this work, an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level. The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends. Then, the latent structure between the degradation features and the RUL labels is modeled by a support vector regression (SVR) model and a long short-term memory (LSTM) network, respectively. To enhance the prediction robustness and increase its marginal utility, the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters. By designing a cost function with penalty mechanism, the three parameters are determined using a modified grey wolf optimization algorithm. In addition, a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method. Verification is done using an aero-engine data set from NASA. The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.

     

    • FullText(HTML)
  • loading
    • References(37)
  • [1]
    C. Hu, H. Pei, X. Si, D. Du, Z. Pang, and X. Wang, “A prognostic model based on DBN and diffusion process for degrading bearing,” IEEE Trans. Ind. Electron., to be published. DOI: 10.1109/TIE.2019.2947839.
    [2]
    P. C. Lopes Gerum, A. Altay, and M. Baykal-Gürsoy, “Data-driven predictive maintenance scheduling policies for railways,” Transp. Res. Pt. C-Emerg. Technol., vol. 107, pp. 137–154, Oct. 2019. doi: 10.1016/j.trc.2019.07.020
    [3]
    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 Proc., vol. 104, pp. 799–834, May 2018. doi: 10.1016/j.ymssp.2017.11.016
    [4]
    K. T. Nguyen and K. Medjaher, “A new dynamic predictive maintenance framework using deep learning for failure prognostics,” Reliab. Eng. Syst. Saf., vol. 188, pp. 251–262, Aug. 2019. doi: 10.1016/j.ress.2019.03.018
    [5]
    K. Wang, “Intelligent predictive maintenance (IPdM) system–Industry 4.0 scenario,” WIT Trans. Eng. Sci., vol. 113, pp. 259–268, 2016.
    [6]
    F. Civerchia, S. Bocchino, C. Salvadori, E. Rossi, L. Maggiani, and M. Petracca, “Industrial Internet of Things monitoring solution for advanced predictive maintenance applications,” J. Ind. Inf. Integr., vol. 7, pp. 4–12, Sept. 2017.
    [7]
    J. Wang, L. Zhang, L. Duan, and R. X. Gao, “A new paradigm of cloudbased predictive maintenance for intelligent manufacturing,” J. Intell. Manuf., vol. 28, no. 5, pp. 1125–1137, Jun. 2017. doi: 10.1007/s10845-015-1066-0
    [8]
    M. Baptista, S. Sankararaman, I. P. de Medeiros, C. Nascimento Jr, H. Prendinger, and E. M. Henriques, “Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling,” Comput. Ind. Eng., vol. 115, pp. 41–53, Jan. 2018. doi: 10.1016/j.cie.2017.10.033
    [9]
    B. O. Gombé, G. G. Mérou, K. Breschi, H. Guyennet, J. M. Friedt, V. Felea, and K. Medjaher, “A SAW wireless sensor network platform for industrial predictive maintenance,” J. Intell. Manuf., vol. 30, no. 4, pp. 1617–1628, Apr. 2019. doi: 10.1007/s10845-017-1344-0
    [10]
    K. A. Nguyen, P. Do, and A. Grall, “Joint predictive maintenance and inventory strategy for multi-component systems using Birnbaum’s structural importance,” Reliab. Eng. Syst. Saf., vol. 168, pp. 249–261, Dec. 2017. doi: 10.1016/j.ress.2017.05.034
    [11]
    S. Khan and T. Yairi, “A review on the application of deep learning in system health management,” Mech. Syst. Signal Proc., vol. 107, pp. 241–265, Jul. 2018. doi: 10.1016/j.ymssp.2017.11.024
    [12]
    C. Zhang, C. Wang, N. Lu, and B. Jiang, “An RBMs-BN method to RUL prediction of traction converter of CRH2 trains,” Eng. Appl. Artif. Intell., vol. 85, pp. 46–56, Oct. 2019. doi: 10.1016/j.engappai.2019.06.001
    [13]
    K. T. Huynh, I. T. Castro, A. Barros, and C. Berenguer, “On the use of mean residual life as a condition index for condition-based maintenance decision-making,” IEEE Trans. Syst. Man Cybern. -Syst., vol. 44, no. 7, pp. 877–893, Jul. 2014. doi: 10.1109/TSMC.2013.2290772
    [14]
    R. Khelif, B. Chebel-Morello, S. Malinowski, E. Laajili, F. Fnaiech, and N. Zerhouni, “Direct remaining useful life estimation based on support vector regression,” IEEE Trans. Ind. Electron., vol. 64, no. 3, pp. 2276–2285, Mar. 2016.
    [15]
    C. Wang, N. Lu, Y. Cheng, and B. Jiang, “A data-driven aero-engine degradation prognostic strategy,” IEEE T. Cybern., to be published. DOI: 10.1109/TCYB.2019.2938244.
    [16]
    E. Ramasso, M. Rombaut, and N. Zerhouni, “Joint prediction of continuous and discrete states in time-series based on belief functions,” IEEE T. Cybern., vol. 43, no. 1, pp. 37–50, Feb. 2013. doi: 10.1109/TSMCB.2012.2198882
    [17]
    Q. Miao, L. Xie, H. Cui, W. Liang, and M. Pecht, “Remaining useful life prediction of lithium-ion battery with unscented particle filter technique,” Microelectron. Reliab., vol. 53, no. 6, pp. 805–810, Jun. 2013. doi: 10.1016/j.microrel.2012.12.004
    [18]
    T. Qin, S. Zeng, and J. Guo, “Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO–SVR model,” Microelectron. Reliab., vol. 55, no. 9–10, pp. 1280–1284, Sept. 2015. doi: 10.1016/j.microrel.2015.06.133
    [19]
    A. Soualhi, K. Medjaher, and N. Zerhouni, “Bearing health monitoring based on Hilbert–Huang transform, support vector machine, and regression,” IEEE Trans. Instrum. Meas., vol. 64, no. 1, pp. 52–62, Jan. 2015. doi: 10.1109/TIM.2014.2330494
    [20]
    H. Dong, X. Jin, Y. Lou, and C. Wang, “Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter,” J. Power Sources, vol. 271, pp. 114–123, Dec. 2014. doi: 10.1016/j.jpowsour.2014.07.176
    [21]
    T. Wang, J. Yu, D. Siegel, and J. Lee, “A similarity-based prognostics approach for remaining useful life estimation of engineered systems,” in Proc. Int. Conf. Prognostics Health Manage., Denver, CO, USA, 2008, pp. 1–6.
    [22]
    S. Malinowski, B. Chebel-Morello, and N. Zerhouni, “Remaining useful life estimation based on discriminating shapelet extraction,” Reliab. Eng. Syst. Saf., vol. 142, pp. 279–288, Oct. 2015. doi: 10.1016/j.ress.2015.05.012
    [23]
    F. Xue, P. Bonissone, A. Varma, W. Yan, N. Eklund, and K. Goebel, “An instance-based method for remaining useful life estimation for aircraft engines,” J. Failure Anal. Prevention, vol. 8, no. 2, pp. 199–206, Apr. 2008. doi: 10.1007/s11668-008-9118-9
    [24]
    E. Zio and F. D. Maio, “A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system,” Reliab. Eng. Syst. Saf., vol. 95, no. 1, pp. 49–57, Jan. 2010. doi: 10.1016/j.ress.2009.08.001
    [25]
    Y. Wu, M. Yuan, S. Dong, L. Lin, and Y. Liu, “Remaining useful life estimation of engineered systems using vanilla LSTM neural networks,” Neurocomputing, vol. 275, pp. 167–179, Jan. 2018. doi: 10.1016/j.neucom.2017.05.063
    [26]
    M. Esmaelian, H. Shahmoradi, and M. Vali, “A novel classification method: A hybrid approach based on extension of the UTADIS with polynomial and PSO-GA algorithm,” Appl. Soft. Comput., vol. 49, pp. 56–70, Dec. 2016. doi: 10.1016/j.asoc.2016.07.017
    [27]
    X. Jia, M. Zhao, Y. Di, Q. Yang, and J. Lee, “Assessment of data suitability for machine prognosis using maximum mean discrepancy,” IEEE Trans. Ind. Electron., vol. 65, no. 7, pp. 5872–5881, Jul. 2018. doi: 10.1109/TIE.2017.2777383
    [28]
    S. Sharifian, and M. Barati, “An ensemble multiscale wavelet-GARCH hybrid SVR algorithm for mobile cloud computing workload prediction,” Int. J. Mach. Learn. Cybern., vol. 10, no. 11, pp. 3285–3300, Nov. 2019. doi: 10.1007/s13042-019-01017-1
    [29]
    C. Wang, N. Lu, S. Wang, Y. Cheng, and B. Jiang, “Dynamic long short-term memory neural-network-based indirect remaining-useful-life prognosis for satellite lithium-ion battery,” Appl. Sci.-Basel, vol. 8, no. 11, Article ID 2078, Nov. 2018. DOI: 10.3390/app8112078.
    [30]
    M. Martínez-García, Y. Zhang, K. Suzuki, and Y. Zhang, “Measuring system entropy with a deep recurrent neural network model,” in Proc. IEEE 17th Int. Conf. Ind. Informat., Helsinki, Finland, 2019, pp. 1253–1256.
    [31]
    M. Martínez-García, Y. Zhang, and T. Gordon, “Memory pattern identification for feedback tracking control in human–machine systems,” Hum. Factors, pp. 1–17, Oct. 2019.
    [32]
    S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, Mar. 2014. doi: 10.1016/j.advengsoft.2013.12.007
    [33]
    H. Faris, I. Aljarah, M. A. Al-Betar, and S. Mirjalili, “Grey wolf optimizer: a review of recent variants and applications,” Neural Comput. Appl., vol. 30, no. 2, pp. 413–435, Jul. 2018. doi: 10.1007/s00521-017-3272-5
    [34]
    NASA. "Prognostic Data Repository". [Online]. Available: https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/. Accessed on: Sept. 2019.
    [35]
    A. Saxena, K. Goebel, D. Simon, and N. Eklund, “Damage propagation modeling for aircraft engine run-to-failure simulation,” in Proc. Int. Conf. Prognostics Health Manage., Denver, CO, USA, 2008, pp. 1–9.
    [36]
    F. O. Heimes, “Recurrent neural networks for remaining useful life estimation,” in Proc. Int. Conf. Prognostics Health Manage., Denver, CO, USA, 2008, pp. 1−6.
    [37]
    R. E. Precup, R. C. David, and E. M. Petriu, “Grey wolf optimizer algorithm-based tuning of fuzzy control systems with reduced parametric sensitivity,” IEEE Trans. Ind. Electron., vol. 64, no. 1, pp. 527–534, Jan. 2017. doi: 10.1109/TIE.2016.2607698
    • 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(12)  / Tables(3)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (2471) PDF downloads(94) Cited by()

    Highlights

    • Degradation feature selection procedure helps to lessen calculative burden.
    • Hybrid model helps to enhance prediction robustness and increase marginal utility.
    • Evolutionary algorithm helps to determine hybrid model parameters.
    • Cost function with penalty mechanism allows alleviating prediction risk.
    • Cost metric allows measuring risk-averse predictive maintenance benefit.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return