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Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion

Author

Listed:
  • Ridha Ziani

    (Ferhat Abbes University Setif 1
    ENST ex CT siege DG SNVI)

  • Ahmed Felkaoui

    (Ferhat Abbes University Setif 1)

  • Rabah Zegadi

    (Ferhat Abbes University Setif 1)

Abstract

Condition monitoring of rotating machinery has attracted more and more attention in recent years in order to reduce the unnecessary breakdowns of components such as bearings and gears which suffer frequently from failures. Vibration based approaches are the most commonly used techniques to the condition monitoring tasks. In this paper, we propose a bearing fault detection scheme based on support vector machine as a classification method and binary particle swarm optimization algorithm (BPSO) based on maximal class separability as a feature selection method. In order to maximize the class separability, regularized Fisher’s criterion is used as a fitness function in the proposed BPSO algorithm. This approach was evaluated using vibration data of bearing in healthy and faulty conditions. The experimental results demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Ridha Ziani & Ahmed Felkaoui & Rabah Zegadi, 2017. "Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 405-417, February.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:2:d:10.1007_s10845-014-0987-3
    DOI: 10.1007/s10845-014-0987-3
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    Citations

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    Cited by:

    1. Tian Wang & Meina Qiao & Mengyi Zhang & Yi Yang & Hichem Snoussi, 2020. "Data-driven prognostic method based on self-supervised learning approaches for fault detection," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1611-1619, October.
    2. Jialin Li & Xueyi Li & David He & Yongzhi Qu, 2020. "Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1899-1916, December.
    3. Yang Hui & Xuesong Mei & Gedong Jiang & Fei Zhao & Ziwei Ma & Tao Tao, 2022. "Assembly quality evaluation for linear axis of machine tool using data-driven modeling approach," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 753-769, March.
    4. Xiang Li & Wei Zhang & Qian Ding & Jian-Qiao Sun, 2020. "Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 433-452, February.
    5. Amine Mezaghcha & Ridha Ziani & Ahmed Felkaoui, 2023. "Empirical wavelet decomposition and BFindex for early detection of bearing defects," Journal of Risk and Reliability, , vol. 237(6), pages 1223-1233, December.
    6. Swapnil K. Gundewar & Prasad V. Kane, 2022. "Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier," 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. 13(6), pages 2876-2894, December.
    7. Dionísio H. C. S. S. Martins & Amaro A. Lima & Milena F. Pinto & Douglas de O. Hemerly & Thiago de M. Prego & Fabrício L. e Silva & Luís Tarrataca & Ulisses A. Monteiro & Ricardo H. R. Gutiérrez & Die, 2023. "Hybrid data augmentation method for combined failure recognition in rotating machines," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1795-1813, April.

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