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DAMER: a novel diagnosis aggregation method with evidential reasoning rule for bearing fault diagnosis

Author

Listed:
  • Gang Wang

    (Hefei University of Technology
    Ministry of Education)

  • Feng Zhang

    (Hefei University of Technology)

  • Bayi Cheng

    (Hefei University of Technology
    Ministry of Education)

  • Fang Fang

    (Hefei University of Technology
    Ministry of Education)

Abstract

Ensemble learning method has shown its superiority in bearing fault diagnosis based on the condition based monitoring. Nevertheless, features extracted from the monitoring signals of bearing systems often contain interrelated and redundant components, leading to poor performances of the base classifiers in the ensemble. Moreover, the current ensemble methods rely on voting strategies to aggregate the diagnostic predictions of these base classifiers without considering their reliabilities and weights simultaneously. To address the aforementioned issues, we propose a novel Diagnosis Aggregation Method with Evidential Reasoning rule, i.e., DAMER, for bearing fault diagnosis. In this method, a semi-random subspace approach using a structured sparsity learning model is developed to decrease the negative effect of interrelated and redundant features, and in the meanwhile to generate accurate and diverse base classifiers. Furthermore, an adaptive evidential reasoning rule (ER rule) incorporating with ensemble learning theory is utilized to aggregate the diagnostic predictions of the base classifiers by taking both their weights and reliabilities into account. To validate the proposed DAMER, an empirical study is conducted on Case Western Reserve University bearing vibration dataset, and the experimental results verify the effectiveness of the proposed DAMER as well as its superiority over commonly used ensemble methods. The performances of feature subsets from multiple domains and the aggregation capability of the adaptive ER rule were also investigated. Results illustrate that DAMER can be utilized as an effective method for bearing fault diagnosis.

Suggested Citation

  • Gang Wang & Feng Zhang & Bayi Cheng & Fang Fang, 2021. "DAMER: a novel diagnosis aggregation method with evidential reasoning rule for bearing fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 1-20, January.
  • Handle: RePEc:spr:joinma:v:32:y:2021:i:1:d:10.1007_s10845-020-01554-5
    DOI: 10.1007/s10845-020-01554-5
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    References listed on IDEAS

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    1. Dong-Ling Xu, 2012. "An introduction and survey of the evidential reasoning approach for multiple criteria decision analysis," Annals of Operations Research, Springer, vol. 195(1), pages 163-187, May.
    2. Manjeevan Seera & Chee Peng Lim & Chu Kiong Loo, 2016. "Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1273-1285, December.
    3. Cong Wang & Meng Gan & Chang’an Zhu, 2017. "Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1377-1391, August.
    4. Pedro Santos & Jesús Maudes & Andres Bustillo, 2018. "Identifying maximum imbalance in datasets for fault diagnosis of gearboxes," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 333-351, February.
    5. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    6. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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