Fault Diagnosis of Rolling Bearing Based on a Priority Elimination Method
Abstract
:1. Introduction
2. Related Methods
2.1. Priority Elimination Method
2.2. SSAE Network
2.3. XGBoost Algorithm
3. Fault Diagnosis Process
4. Experimental Validation
4.1. Experimental Data
4.2. Priority Diagnosis Sequence
4.3. Diagnosis Results
5. Conclusions
- (1)
- In terms of the improvement of the fault diagnosis accuracy, PE improves the fault diagnosis accuracy of all methods. The SSAE-XGBoost model combined with the PE method increases the fault diagnosis accuracy from 96.3% to 99.27%, which is also significantly higher than some classical algorithms with or without PE.
- (2)
- In the aspect of the identification of unknown faults, the fault data that do not appear in the training set are put into the test set. SSAE-XGBoost with PE can improve the accuracy of fault diagnosis from 86.96% to 92.34%, which is superior to other classical fault diagnosis methods with or without PE.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Fault Type | Fault Diameter/mm | Collecting Position | Sample Number | Label |
---|---|---|---|---|
IRF | 0.1778 | DE | 102,400 | 1 |
FE | 102,400 | 1 | ||
0.3556 | DE | 102,400 | 2 | |
FE | 102,400 | 2 | ||
0.5334 | DE | 102,400 | 3 | |
FE | 102,400 | 3 | ||
ORF | 0.1778 | DE | 102,400 | 4 |
FE | 102,400 | 4 | ||
0.3556 | DE | 102,400 | 5 | |
FE | 102,400 | 5 | ||
0.5334 | DE | 102,400 | 6 | |
FE | 102,400 | 6 | ||
BAF | 0.1778 | DE | 102,400 | 7 |
FE | 102,400 | 7 | ||
0.3556 | DE | 102,400 | 8 | |
FE | 102,400 | 8 | ||
0.5334 | DE | 102,400 | 9 | |
FE | 102,400 | 9 | ||
Normal state | 0 | DE | 204,800 | 0 |
Fault Type | IRF | BAF | ORF |
---|---|---|---|
Average ratio of Sb to Sw | 418.17 | 291.37 | 361.22 |
SSAE | Number of hidden layers | 3 |
Network structure | 1024-512-256-128-10 | |
Optimizer | Adam | |
Iterations | 60 | |
XGBoost | Max depth | 5 |
Min child weight | 1 | |
N estimators | 80 | |
Min child weight | 0.12 |
Methods | Accuracy (%) | Methods | Accuracy (%) |
---|---|---|---|
SSAE-XGBoost | 96.30 | PE-SSAE-XGBoost | 99.27 |
CNN | 96.82 | PE-CNN | 97.53 |
SVM | 94.78 | PE-SVM | 95.26 |
DBN | 93.19 | PE-DBN | 95.67 |
Methods | Accuracy (%) | Methods | Accuracy (%) |
---|---|---|---|
SSAE-XGBoost | 86.96 | PE-SSAE-XGBoost | 92.34 |
CNN | 84.19 | PE-CNN | 90.27 |
SVM | 82.42 | PE-SVM | 89.51 |
DBN | 81.03 | PE-DBN | 89.21 |
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Share and Cite
Xiang, C.; Zhou, J.; Han, B.; Li, W.; Zhao, H. Fault Diagnosis of Rolling Bearing Based on a Priority Elimination Method. Sensors 2023, 23, 2320. https://doi.org/10.3390/s23042320
Xiang C, Zhou J, Han B, Li W, Zhao H. Fault Diagnosis of Rolling Bearing Based on a Priority Elimination Method. Sensors. 2023; 23(4):2320. https://doi.org/10.3390/s23042320
Chicago/Turabian StyleXiang, Chuan, Jiahui Zhou, Bing Han, Weichen Li, and Hongge Zhao. 2023. "Fault Diagnosis of Rolling Bearing Based on a Priority Elimination Method" Sensors 23, no. 4: 2320. https://doi.org/10.3390/s23042320
APA StyleXiang, C., Zhou, J., Han, B., Li, W., & Zhao, H. (2023). Fault Diagnosis of Rolling Bearing Based on a Priority Elimination Method. Sensors, 23(4), 2320. https://doi.org/10.3390/s23042320