Bearing fault diagnosis method based on dual-channel feature fusion
Intelligent diagnosis method based on convolution neural network (CNN) has been widely used in bearing fault diagnosis. However, most existing diagnostic models rely on single-source information inputs, limiting their accuracy and reliability. To solve this limitation, this paper presents a rolling...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2023-11-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2023.06.005 |
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| Summary: | Intelligent diagnosis method based on convolution neural network (CNN) has been widely used in bearing fault diagnosis. However, most existing diagnostic models rely on single-source information inputs, limiting their accuracy and reliability. To solve this limitation, this paper presents a rolling bearing fault diagnosis method based on dual-channel feature fusion. Firstly, the time-frequency analysis diagrams of rolling bearing vibration signals were constructed by using multiple Q-factor continuous Gabor wavelet transform (CMQGWT) and fast spectral coherence (Fast-SC), respectively. Subsequently, a CNN model with dual input channels was constructed, allowing for the fusion of deep time-frequency features extracted from each channel into a new feature at a feature fusion layer. Finally, the diagnosis results were output using a classifier. Through classification and recognition experiments involving single and compound faults in rolling bearings for high-speed trains, compared with the CNN model with a single input channel, the proposed model demonstrates superior diagnostic accuracy and robustness. |
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| ISSN: | 1000-128X |