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Intelligent Robust Cross-Domain Fault Diagnostic Method for Rotating Machines Using Noisy Condition Labels

Author

Listed:
  • Abhijeet Ainapure

    (Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH 45221, USA)

  • Shahin Siahpour

    (Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH 45221, USA)

  • Xiang Li

    (Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, China)

  • Faray Majid

    (Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH 45221, USA)

  • Jay Lee

    (Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH 45221, USA)

Abstract

Cross-domain fault diagnosis methods have been successfully and widely developed in the past years, which focus on practical industrial scenarios with training and testing data from numerous machinery working regimes. Due to the remarkable effectiveness in such problems, deep learning-based domain adaptation approaches have been attracting increasing attention. However, the existing methods in the literature are generally lower compared to environmental noise and data availability, and it is difficult to achieve promising performance under harsh practical conditions. This paper proposes a new cross-domain fault diagnosis method with enhanced robustness. Noisy labels are introduced to significantly increase the generalization ability of the data-driven model. Promising diagnosis performance can be obtained with strong noise interference in testing, as well as in practical cases with low-quality data. Experiments on two rotating machinery datasets are carried out for validation. The results indicate that the proposed algorithm is well suited to be applied in real industrial environments to achieve promising performance with variations of working conditions.

Suggested Citation

  • Abhijeet Ainapure & Shahin Siahpour & Xiang Li & Faray Majid & Jay Lee, 2022. "Intelligent Robust Cross-Domain Fault Diagnostic Method for Rotating Machines Using Noisy Condition Labels," Mathematics, MDPI, vol. 10(3), pages 1-17, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:455-:d:739011
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    References listed on IDEAS

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    1. Gyanendra Prasad Joshi & Fayadh Alenezi & Gopalakrishnan Thirumoorthy & Ashit Kumar Dutta & Jinsang You, 2021. "Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks," Mathematics, MDPI, vol. 9(22), pages 1-17, November.
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    Cited by:

    1. Dezhi Hao & Xianwen Gao, 2022. "Unsupervised Fault Diagnosis of Sucker Rod Pump Using Domain Adaptation with Generated Motor Power Curves," Mathematics, MDPI, vol. 10(8), pages 1-22, April.

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