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Fault diagnosis for machinery based on feature extraction and general regression neural network

Author

Listed:
  • Haiping Li

    (Mechanical Engineering College)

  • Jianmin Zhao

    (Mechanical Engineering College)

  • Xianglong Ni

    (Mechanical Engineering College)

  • Xinghui Zhang

    (Mechanical Engineering College)

Abstract

Fault diagnosis for the maintenance of machinery is more difficult since it becomes more precise, automatic and efficient. To tackle this problem, a new feature extraction method for signal processing is developed and a general regression neural network (GRNN)—based method is proposed in this paper. Features are extracted from vibration signals that collected from mechanical systems and a feature selection method based on Euclidean distance technique (EDT) is applied. Then, the selected features are processed by the fault characteristic frequencies of mechanical components. And a part of processed data is as train samples and the others as test samples. Finally, the samples are inputted to GRNN to train and verify the model. The proposed method is applied as a fault diagnosis method for both planetary gearbox and bearings datasets, and the performance of it is validated by compared to such methods as radial basis function neural networks (RBFNN), probabilistic neural network (PNN) and a combination model (EMD–EDT). The experimental results show that the GRNN-based method has an advantage over other similar approaches.

Suggested Citation

  • Haiping Li & Jianmin Zhao & Xianglong Ni & Xinghui Zhang, 2018. "Fault diagnosis for machinery based on feature extraction and general regression neural network," 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. 9(5), pages 1034-1046, October.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:5:d:10.1007_s13198-018-0726-9
    DOI: 10.1007/s13198-018-0726-9
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    References listed on IDEAS

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    1. Jiang, Yonghua & Tang, Baoping & Qin, Yi & Liu, Wenyi, 2011. "Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD," Renewable Energy, Elsevier, vol. 36(8), pages 2146-2153.
    2. Chen Lu & Yang Wang & Minvydas Ragulskis & Yujie Cheng, 2016. "Fault Diagnosis for Rotating Machinery: A Method based on Image Processing," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-22, October.
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    Cited by:

    1. Xiaowen Wang & Ying Ma & Wen Li, 2021. "The Prediction of Gold Futures Prices at the Shanghai Futures Exchange Based on the MEEMD-CS-Elman Model," SAGE Open, , vol. 11(1), pages 21582440211, March.

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