Motor Bearing Fault Diagnosis Based on Current Signal Using Time–Frequency Channel Attention
Abstract
:1. Introduction
- In view of the weak characteristics of the current signal, a data enhancement method of periodic sampling of the current signal is proposed at the data end, and the enhanced data are spliced and fused;
- In the input layer of the model, a multi-scale feature fusion method that can enhance fault features by self-learning is proposed. Through dimensional transformation of the input, multiple convolution layers decompose the signal. Finally, more fault features are provided through the fusion operation;
- Aiming at the problem of the poor classification performance of existing diagnostic models, an improved 1DCNN is constructed by extending the CAM to the time domain and frequency domain.
2. Biphasic Current Signal Fusion
2.1. Periodic Sampling
2.2. Multi-Channel Fusion
2.3. Ablation Experiment
3. Multi-Scale Feature Fusion
3.1. Feature Enhance
3.2. Ablation Experiment
4. Improve Channel Attention Module
4.1. Improve Channel Attention Module Structure
4.2. Ablation Experiment
5. Experiment and Analysis
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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No. | Speed (r/min) | Load Torque (N·m) | Radial Force (N) |
---|---|---|---|
0 | 1500 | 0.7 | 1000 |
1 | 900 | 0.7 | 1000 |
2 | 1500 | 0.1 | 1000 |
3 | 1500 | 0.7 | 400 |
Dataset | Accuracy (%) | Training Times (Seconds) |
---|---|---|
M0 | 93.35 | 23.60 |
M1 | 94.58 | 24.14 |
M2 | 92.42 | 24.31 |
M3 | 93.57 | 29.59 |
M4 | 92.64 | 47.76 |
M5 | 93.03 | 19.17 |
Label | Fault Size (mm) | Fault Type |
---|---|---|
IR1 | 0.6 | Inner |
IR2 | 1.2 | Inner |
NOR | Null | Normal |
BA | 0.6 | Ball |
OR1 | 0.6 | Outer |
OR2 | 1.2 | Outer |
No. | Speed (r/min) | Load Torque (N·m) | SWF (kHz) |
---|---|---|---|
0 | 1500 | 4 | 16 |
1 | 900 | 2 | 16 |
2 | 1500 | 6 | 16 |
3 | 1500 | 4 | 24 |
4 | 900 | 2 | 24 |
5 | 1500 | 6 | 24 |
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Share and Cite
Wang, Z.; Guan, C.; Shi, S.; Zhang, G.; Gu, X. Motor Bearing Fault Diagnosis Based on Current Signal Using Time–Frequency Channel Attention. World Electr. Veh. J. 2024, 15, 281. https://doi.org/10.3390/wevj15070281
Wang Z, Guan C, Shi S, Zhang G, Gu X. Motor Bearing Fault Diagnosis Based on Current Signal Using Time–Frequency Channel Attention. World Electric Vehicle Journal. 2024; 15(7):281. https://doi.org/10.3390/wevj15070281
Chicago/Turabian StyleWang, Zhiqiang, Chao Guan, Shangru Shi, Guozheng Zhang, and Xin Gu. 2024. "Motor Bearing Fault Diagnosis Based on Current Signal Using Time–Frequency Channel Attention" World Electric Vehicle Journal 15, no. 7: 281. https://doi.org/10.3390/wevj15070281