Research on Small Sample Rolling Bearing Fault Diagnosis Method Based on Mixed Signal Processing Technology
Abstract
:1. Introduction
- Migration learning: The method employs models that have been pre-trained on large-scale datasets and subsequently fine-tuned to accommodate small sample datasets. This approach fully exploits the existing knowledge and mitigates the reliance on limited sample datasets [32].
- Generative Adversarial Networks (GANs): It is possible to enhance the diversity of the sample set by developing models that can generalize more effectively when dealing with smaller sample sizes. Generative Adversarial Networks (GANs) can create virtual samples that resemble real ones, allowing the training dataset to be expanded [33].
- Feature selection and dimensionality reduction: In a limited number of samples, feature selection and transformed dimensionality techniques remove redundant information and retain the most useful features for fault diagnosis, improving model performance [34].
2. Relevant Methodologies
2.1. Short-Time Fourier Transforms
2.2. Synchronized Compressed Wavelet Transform
2.3. Dual-Channel Parallel CNN Model with Symmetry
3. Diagnostic Methods and Processes
- (1)
- Data processing: The overlapping intercepts of signals, adding labels, and obtaining sample sets.
- (2)
- The sample set’s vibration signals undergo a short-time Fourier transform and a synchronized compressed wavelet transform, creating an image for each sample to form the feature sample set. This feature sample set is then randomly split into a training set and a test set based on a specified ratio.
- (3)
- A parallel CNN model is constructed and the model parameters are initialized.
- (4)
- The dual-channel parallel CNN training model processes both the training and test sets at the same time. During each iteration of training, the model is validated using the test set to monitor its convergence. After completing the necessary iterations and reaching convergence, the model is saved for future evaluation.
- (5)
- Once the training is complete, the model’s performance is assessed using the test set.
4. Experimental Section
4.1. CWRU Dataset
4.2. Data Generation and Model Training
4.2.1. Data Generation
4.2.2. Model Training
4.2.3. Fault Diagnosis with Different Sample Sizes
4.3. Advantages of Integrated Processing Methods and Parallel Networks
4.4. Sample Size and Fault Diagnosis Accuracy Experiment
4.5. Exploring Generalizability
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Fault Diameter/mm | Type of Fault | Fault Category | Sample Size |
---|---|---|---|
0 | N | 1 | 100 |
0.007 | I1 | 2 | 100 |
0.014 | I2 | 3 | 100 |
0.021 | I3 | 4 | 100 |
0.007 | B1 | 5 | 100 |
0.014 | B2 | 6 | 100 |
0.021 | B3 | 7 | 100 |
0.007 | O1 | 8 | 100 |
0.014 | O2 | 9 | 100 |
0.021 | O3 | 10 | 100 |
Experiment Number | Sample Size | Accuracy | F1 Score |
---|---|---|---|
1 | 700 | 99.52% | 99.57% |
2 | 500 | 99.33% | 99.37% |
3 | 300 | 98.89% | 98.91% |
4 | 200 | 98.33% | 98.37% |
Mold | Treatment | Accuracy |
---|---|---|
CNN | SCWT | 97.34% |
CNN-SVM | 98.00% | |
CNN | SFTF | 94.28% |
CNN-SVM | 98.67% | |
SVM | SCWT-SFTF | 85.63% |
PCNN | 98.67% | |
PCNN-SVM | 99.34% |
Sample Size | SFTF-CNN | SCWT-CNN | SCWT-CNN-SVM | STFT-CNN-SVM | SCWT-SFTF-PCNN | SCWT-SFTF-PCNN-SVM |
---|---|---|---|---|---|---|
200 | 81.38% | 82.5% | 77.38% | 81.86% | 93.75% | 98.43% |
300 | 92.84% | 94.57% | 95.37% | 93.71% | 96.29% | 98.57% |
500 | 93.61% | 96.60% | 96.80% | 98.67% | 98.38% | 98.67% |
700 | 94.28% | 97.34% | 98.00% | 98.67% | 98.67% | 99.34% |
Bearing Condition | Operational State | Fault Category | Sample Size |
---|---|---|---|
Well-being | expedite | 1 | 100 |
decelerations | 2 | 100 | |
Inner-ring failure | expedite | 3 | 100 |
decelerations | 4 | 100 | |
Outer-ring failure | expedite | 5 | 100 |
decelerations | 6 | 100 | |
Ball fault | expedite | 7 | 100 |
decelerations | 8 | 100 | |
Composite fault | expedite | 9 | 100 |
decelerations | 10 | 100 |
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Yu, P.; Zhang, J.; Zhang, B.; Cao, J.; Peng, Y. Research on Small Sample Rolling Bearing Fault Diagnosis Method Based on Mixed Signal Processing Technology. Symmetry 2024, 16, 1178. https://doi.org/10.3390/sym16091178
Yu P, Zhang J, Zhang B, Cao J, Peng Y. Research on Small Sample Rolling Bearing Fault Diagnosis Method Based on Mixed Signal Processing Technology. Symmetry. 2024; 16(9):1178. https://doi.org/10.3390/sym16091178
Chicago/Turabian StyleYu, Peibo, Jianjie Zhang, Baobao Zhang, Jianhui Cao, and Yihang Peng. 2024. "Research on Small Sample Rolling Bearing Fault Diagnosis Method Based on Mixed Signal Processing Technology" Symmetry 16, no. 9: 1178. https://doi.org/10.3390/sym16091178