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Unified discriminant manifold learning for rotating machinery fault diagnosis

Author

Listed:
  • Changyuan Yang

    (Shandong University
    Ministry of Education
    National Demonstration Center for Experimental Mechanical Engineering Education at Shandong University
    Qilu Hospital of Shandong University)

  • Sai Ma

    (Shandong University
    Ministry of Education
    National Demonstration Center for Experimental Mechanical Engineering Education at Shandong University
    Qilu Hospital of Shandong University)

  • Qinkai Han

    (Tsinghua University)

Abstract

Fault diagnosis is an important technology for performing intelligent manufacturing. To simultaneously maintain high manufacturing quality and low failure rate for manufacturing systems, it is of great value to accurately locate the fault element, evaluate the fault severity and find the fault root cause. In order to effectively and accurately perform fault diagnosis for rotating machinery, a novel feature selection method named unified discriminant manifold learning (UDML) is proposed in this research. To be specific, the local linear relationship, the distance between adjacent points, the intra-class and inter-class variance are unified in UDML. Based on these, the local structure, global information and label information of high-dimensional features are effectively preserved by UDML. Through this dimension reduction method, homogeneous features become more concentrated while heterogeneous features become more distant. Consequently, mechanical faults could be diagnosed accurately with the help of proposed UDML. More importantly, local linear embedding algorithm, locality preserving projections algorithm, and linear discriminant analysis algorithm could be regarded as a special form of UDML. Moreover, a novel weighted neighborhood graph is constructed to effectively reduce the interference of outliers and noise. The corresponding model parameters are dynamically adjusted by the gray wolf optimization algorithm to find a subspace that discovers the intrinsic manifold structure for classification tasks. Based on the above innovations, a fault diagnosis method for rotating machinery is proposed. Through experimental verifications and comparisons with several classical feature selection algorithms, rotating machinery fault diagnosis can be more accurately performed by the proposed method.

Suggested Citation

  • Changyuan Yang & Sai Ma & Qinkai Han, 2023. "Unified discriminant manifold learning for rotating machinery fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3483-3494, December.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:8:d:10.1007_s10845-022-02011-1
    DOI: 10.1007/s10845-022-02011-1
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    References listed on IDEAS

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    1. Edward H. Nieh & Manuel Schottdorf & Nicolas W. Freeman & Ryan J. Low & Sam Lewallen & Sue Ann Koay & Lucas Pinto & Jeffrey L. Gauthier & Carlos D. Brody & David W. Tank, 2021. "Geometry of abstract learned knowledge in the hippocampus," Nature, Nature, vol. 595(7865), pages 80-84, July.
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