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An unsupervised mechanical fault classification method under the condition of unknown number of fault types

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Abstract

This paper proposes a novel unsupervised classification method to solve the problem of mechanical fault diagnosis under the condition of unknown number of fault types. The proposed method combining the three data processing stage. First, the deep encoding neural networks is used to complete the abstract signal feature representation under the unsupervised conditions. Second, the feature dimensionality reduction technique based on manifold learning is used to complete the low-dimensional mapping of the feature space. Third, the spatial clustering based on density criterion is introduced to classify the different fault samples. This paper uses two fault signals dataset to complete the performance verification experiment. The experimental results show that the DMDUC method respectively achieves the classification accuracy of 99.7 % and 100 % on the two datasets.

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Abbreviations

SR :

Sparse regularization metric

KLD :

Kullback-Leiber divergence

DENN :

Deep encoding neural networks

ML :

Manifold learning

MFES :

Multi-functional fault experiment stand

HF :

Handcrafted features

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Acknowledgments

This work is supported by The National Natural Science Foundation of China (Project No. 11904407), China.

Author information

Authors and Affiliations

  1. Combat Command Department, Naval Command College, Nanjing, China

    Yalun Zhang

  2. Laboratory of Vibration and Noise, Naval University of Engineering, Wuhan, China

    Rongwu Xu, Guo Cheng, Xiufeng Huang & Wenjing Yu

Authors
  1. Yalun Zhang
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  2. Rongwu Xu
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  3. Guo Cheng
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  4. Xiufeng Huang
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  5. Wenjing Yu
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Corresponding author

Correspondence to Wenjing Yu.

Additional information

Yalun Zhang is a lecturer of Combat Command Department, Naval Command College, Nanjing, China. He received his Ph.D. in Naval University of Engineering. His research interests include fault diagnosis, signal analysis, machine learning.

Rongwu Xu is a Professor of Laboratory of Vibration and Noise, Naval University of Engineering, Wuhan, China. He received his Ph.D. in Naval University of Engineering. His research interests include vibration & noise control, acoustic theory, signal analysis.

Guo Cheng is a Professor of Laboratory of Vibration and Noise, Naval University of Engineering, Wuhan, China. He received his Ph.D. in Naval University of Engineering. His research interests include vibration & noise control, acoustic theory, signal analysis.

Xiufeng Huang is an ABD of Laboratory of Vibration and Noise, Naval Uni-versity of Engineering, Wuhan, China. He received his Ph.D. in Naval University of Engineering. Her research interests include vibration & noise control, acoustic theory.

Wenjing Yu is a Doctor of Laboratory of Vibration and Noise, Naval University of Engineering, Wuhan, China. She received his Ph.D. in Naval University of Engineering. Her research interests include vibration & noise control, signal analysis, fault diagnosis.

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Zhang, Y., Xu, R., Cheng, G. et al. An unsupervised mechanical fault classification method under the condition of unknown number of fault types. J Mech Sci Technol 38, 605–622 (2024). https://doi.org/10.1007/s12206-024-0109-x

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  • DOI: https://doi.org/10.1007/s12206-024-0109-x

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