Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults

Aiming at the difficulty of extracting and classifying early bearing faults, a fault diagnosis method based on weighted average time-varying filtering empirical mode decomposition and improved eigenclass is proposed in this paper. Firstly, the bearing fault signal is decomposed into a series of intr...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021-11, Vol.2021, p.1-11
Main Authors: Du, Zhengyu, Ma, Jie, Ma, Chao, Huang, Min, Sun, Weiwei
Format: Article
Language:English
Subjects:
Accuracy
Acoustics
Classification
Decomposition
Fault detection
Fault diagnosis
Fourier transforms
Machine learning
Methods
Neural networks
Optimization
Sensors
Signal classification
Signal to noise ratio
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Summary:Aiming at the difficulty of extracting and classifying early bearing faults, a fault diagnosis method based on weighted average time-varying filtering empirical mode decomposition and improved eigenclass is proposed in this paper. Firstly, the bearing fault signal is decomposed into a series of intrinsic mode functions by the signal decomposition method, and the amplitude of the component is modulated by the weighted average method to enhance the fault impulse component. Then, the fractional Fourier transform is used to filter the reconstructed signal. Regarding classification issues, the eigenclass classifier is optimized by the IDE method that can be used for feature dimensionality reduction. Finally, the optimal features are selected and input into the IDE-EigenClass model. The experimental results show that the bearing fault diagnosis method proposed in this paper has higher accuracy and stability than the traditional PNN, SVM, BP, and other methods.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/5503107