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Using principal components in a proportional hazards model with applications in condition-based maintenance

Author

Listed:
  • D Lin

    (University of Toronto)

  • D Banjevic

    (University of Toronto)

  • A K S Jardine

    (University of Toronto)

Abstract

This paper proposes the application of a principal components proportional hazards regression model in condition-based maintenance (CBM) optimization. The Cox proportional hazards model with time-dependent covariates is considered. Principal component analysis (PCA) can be applied to covariates (measurements) to reduce the number of variables included in the model, as well as to eliminate possible collinearity between the covariates. The main issues and problems in using the proposed methodology are discussed. PCA is applied to a simulated CBM data set and two real data sets obtained from industry: oil analysis data and vibration data. Reasonable results are obtained.

Suggested Citation

  • D Lin & D Banjevic & A K S Jardine, 2006. "Using principal components in a proportional hazards model with applications in condition-based maintenance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(8), pages 910-919, August.
    • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:8:d:10.1057_palgrave.jors.2602058
    DOI: 10.1057/palgrave.jors.2602058
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    References listed on IDEAS

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    1. Ian T. Jolliffe, 1982. "A Note on the Use of Principal Components in Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(3), pages 300-303, November.
    2. P J Vlok & J L Coetzee & D Banjevic & A K S Jardine & V Makis, 2002. "Optimal component replacement decisions using vibration monitoring and the proportional-hazards model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(2), pages 193-202, February.
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    Cited by:

    1. Jia, Xiaodong & Jin, Chao & Buzza, Matt & Wang, Wei & Lee, Jay, 2016. "Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves," Renewable Energy, Elsevier, vol. 99(C), pages 1191-1201.
    2. Peng, Hao & van Houtum, Geert-Jan, 2016. "Joint optimization of condition-based maintenance and production lot-sizing," European Journal of Operational Research, Elsevier, vol. 253(1), pages 94-107.
    3. You, Ming-Yi & Li, Hongguang & Meng, Guang, 2011. "Control-limit preventive maintenance policies for components subject to imperfect preventive maintenance and variable operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 590-598.
    4. Mohammad Ali Farsi & S. Masood Hosseini, 2019. "Statistical distributions comparison for remaining useful life prediction of components via ANN," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(3), pages 429-436, June.
    5. Dao, Cuong D. & Zuo, Ming J., 2017. "Optimal selective maintenance for multi-state systems in variable loading conditions," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 171-180.
    6. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    7. Zhou, Dengji & Zhang, Huisheng & Weng, Shilie, 2014. "A novel prognostic model of performance degradation trend for power machinery maintenance," Energy, Elsevier, vol. 78(C), pages 740-746.
    8. Braglia, Marcello & Carmignani, Gionata & Frosolini, Marco & Zammori, Francesco, 2012. "Data classification and MTBF prediction with a multivariate analysis approach," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 27-35.
    9. Tian, Zhigang & Liao, Haitao, 2011. "Condition based maintenance optimization for multi-component systems using proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 581-589.
    10. Chrianna I Bharat & Kevin Murray & Edward Cripps & Melinda R Hodkiewicz, 2018. "Methods for displaying and calibration of Cox proportional hazards models," Journal of Risk and Reliability, , vol. 232(1), pages 105-115, February.

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