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Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions

Author

Listed:
  • Ahmed Ragab

    (École Polytechnique de Montréal)

  • Soumaya Yacout

    (École Polytechnique de Montréal)

  • Mohamed-Salah Ouali

    (École Polytechnique de Montréal)

  • Hany Osman

    (École Polytechnique de Montréal)

Abstract

This paper presents a novel methodology for multiple failure modes prognostics in rotating machinery. The methodology merges a machine learning and pattern recognition approach, called logical analysis of data (LAD), with non-parametric cumulative incidence functions (CIFs). It considers the condition monitoring data collected from a system that experiences several competing failure modes over its life span. LAD is used as a non-statistical classification technique to detect the actual state of the system, based on the condition monitoring data. The CIF provides an estimate for the marginal probability of each failure mode in the presence of the other competing failure modes. Accordingly, the assumption of independence between the failure modes, which is essential in many prognostic methods, is irrelevant in this paper. The proposed methodology is validated using vibration data collected from bearing test rigs. The obtained results are compared to those of two common machine learning prediction techniques: the artificial neural network and support vector regression. The comparison shows that the proposed methodology has a stable performance and can predict the remaining useful life of an individual system accurately, in the presence of multiple failure modes.

Suggested Citation

  • Ahmed Ragab & Soumaya Yacout & Mohamed-Salah Ouali & Hany Osman, 2019. "Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 255-274, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1244-8
    DOI: 10.1007/s10845-016-1244-8
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    References listed on IDEAS

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    1. Wang, Chaonan & Xing, Liudong & Levitin, Gregory, 2013. "Reliability analysis of multi-trigger binary systems subject to competing failures," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 9-17.
    2. Bocchetti, D. & Giorgio, M. & Guida, M. & Pulcini, G., 2009. "A competing risk model for the reliability of cylinder liners in marine Diesel engines," Reliability Engineering and System Safety, Elsevier, vol. 94(8), pages 1299-1307.
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    4. Xiao Liu & Jingrui Li & Khalifa Al-Khalifa & Abdelmagid Hamouda & David Coit & Elsayed Elsayed, 2013. "Condition-based maintenance for continuously monitored degrading systems with multiple failure modes," IISE Transactions, Taylor & Francis Journals, vol. 45(4), pages 422-435.
    5. Xing, Liudong & Levitin, Gregory, 2010. "Combinatorial analysis of systems with competing failures subject to failure isolation and propagation effects," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1210-1215.
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    Cited by:

    1. Mohamed Elhefnawy & Ahmed Ragab & Mohamed-Salah Ouali, 2023. "Polygon generation and video-to-video translation for time-series prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 261-279, January.
    2. Qifa Xu & Shixiang Lu & Weiyin Jia & Cuixia Jiang, 2020. "Imbalanced fault diagnosis of rotating machinery via multi-domain feature extraction and cost-sensitive learning," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1467-1481, August.
    3. Mikhail, Mina & Ouali, Mohamed-Salah & Yacout, Soumaya, 2024. "A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Feng Feng & Meng Yuan & Yousheng Xia & Haoming Xu & Pingfa Feng & Xinghui Li, 2022. "Roughness Scaling Extraction Accelerated by Dichotomy-Binary Strategy and Its Application to Milling Vibration Signal," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    5. Mohamed Elhefnawy & Ahmed Ragab & Mohamed-Salah Ouali, 2022. "Fault classification in the process industry using polygon generation and deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1531-1544, June.
    6. Karim Nadim & Ahmed Ragab & Mohamed-Salah Ouali, 2023. "Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 57-83, January.

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