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Bearing Fault Diagnosis Based on Discriminant Analysis Using Multi-View Learning

Author

Listed:
  • Zhe Tong

    (School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China)

  • Wei Li

    (School of Mechanical Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Bo Zhang

    (School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China)

  • Haifeng Gao

    (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Xinglong Zhu

    (School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China)

  • Enrico Zio

    (Energy Department, Politecnico di Milano, Via Privata Giuseppe La Masa, 20156 Milano, Italy)

Abstract

Bearing fault diagnosis has been a challenge in rotating machinery and has gained considerable attention. In order to correctly classify faults, the conventional fault diagnosis methods are mostly based on vibration signals. However, features extracted from a single view of vibration signals may leave out useful information, which can cause the incompleteness of intrinsic information and increase the risk of the performance degradation of fault classifications. In this paper, a novel bearing fault diagnosis method, discriminant analysis using multi-view learning (DAML), is proposed to tackle this issue. Multi-view datasets referring to vibration and acoustic signals are obtained by carrying out a fast Fourier transform (FFT). Then, multi-view feature (MVF) representation, including view-invariant and category discriminative information in a common subspace, is achieved based on canonical correlation analysis (CCA) and uncorrelated linear discriminant analysis (ULDA). Ultimately, with the help of the K-nearest neighbor (KNN) classifier built on the multi-view features, bearing faults are identified. The extensive experimental results show that DAML can identify the bearing fault accurately and outperforms other competitive approaches.

Suggested Citation

  • Zhe Tong & Wei Li & Bo Zhang & Haifeng Gao & Xinglong Zhu & Enrico Zio, 2022. "Bearing Fault Diagnosis Based on Discriminant Analysis Using Multi-View Learning," Mathematics, MDPI, vol. 10(20), pages 1-17, October.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3889-:d:947684
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    Cited by:

    1. Changchun Mo & Huizi Han & Mei Liu & Qinghua Zhang & Tao Yang & Fei Zhang, 2023. "Research on SVM-Based Bearing Fault Diagnosis Modeling and Multiple Swarm Genetic Algorithm Parameter Identification Method," Mathematics, MDPI, vol. 11(13), pages 1-28, June.

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