Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis
Abstract
:1. Introduction
- In the feature extraction stage, three feature extraction methods, MRA, FFT, and EA, are used to effectively extract features from the original signal to form a feature set.
- A proposal for a feature selection method for BSSA with an extended repository, which is beneficial to maintaining racial diversity.
- This study validates the proposed model through four datasets. The results show that the SVM classifier is more suitable for this model than the KNN classifier.
2. Feature Extraction Methods
2.1. Multi-Resolution Analysis
2.2. Fast Fourier Transform
2.3. Envelope Analysis
3. The Feature Selection Method
3.1. Binary Salp Swarm Algorithm
3.2. Updating the Salp’s Position Based on Feature Weights
3.3. Updating the Slap Position Based on Genetic Operators
3.4. The Proposed BSSA-ER Method
4. The Bearing Fault Diagnosis Model
5. Experimental Verification and Results
5.1. Case Study 1: UCI Benchmark Datasets
5.1.1. Description of the Datasets
5.1.2. Comparison with Other Feature Selection Methods
5.2. Case Study 2: Motor Dataset of Current Signal
5.2.1. Description of the Dataset
5.2.2. Comparison with Other Feature Selection Methods
5.3. Case Study 3: CWRU Benchmark Dataset
5.3.1. Description of the Dataset
5.3.2. Comparison with Other Feature Selection Methods
5.4. Case Study 4: MFPT Benchmark Dataset
5.4.1. Description of the Dataset
5.4.2. Comparison with Other Feature Selection Methods
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Features | MRA d1 d2 d3 d4 d5 a1 a2 a3 a4 a5 | FFT d1 d2 d3 d4 d5 a1 a2 a3 a4 a5 | EA |
---|---|---|---|
Max | F1, F2, F3, F4, F5 F6, F7, F8, F9, F10 | F11, F12, F13, F14, F15 F16, F17, F18, F19, F20 | F101, F102, |
Min | F21, F22, F23, F24, F25 F26, F27, F28, F29, F30 | F31, F32, F36, F34, F35 F36, F37, F38, F39, F40 | F103, F104, |
Mean | F41, F42, F43, F44, F45 F46, F47, F48, F49, F50 | F51, F52, F53, F54, F55 F56, F57, F58, F59, F60 | F105, F106, |
Mse | F61, F62, F63, F64, F65 F66, F67, F68, F69, F70 | F71, F72, F73, F74, F75 F76, F77, F78, F79, F80 | F107, F108, |
Std | F81, F82, F83, F84, F85 F86, F87, F88, F89, F90 | F91, F92, F93, F94, F95 F96, F97, F98, F99, F100 | F109, F110 |
Datasets | Features | Instances | Classes |
---|---|---|---|
BreastEW | 30 | 569 | 2 |
WaveformEW | 40 | 5000 | 2 |
Sonar | 60 | 208 | 2 |
Vote | 16 | 300 | 2 |
Vehicle | 18 | 94 | 5 |
HeartEW | 13 | 270 | 2 |
Datasets | Best Accuracy | Number of Feature | Feature Index |
---|---|---|---|
BreastEW | 95.42% | 16 | F2, F3, F6, F11, F12, F15, F16, F19, F21, F23, F25, F26, F27, F28, F29, F30 |
WaveformEW | 83.66% | 27 | F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, F11, F12, F13, F14, F15, F16, F17, F19, F21, F29, F30, F31, F36, F37, F38, F39, F40 |
Sonar | 87.98% | 35 | F1, F3, F4, F7, F9, F10, F11, F12, F13, F15, F16, F22, F23, F24, F26, F30, F31, F32, F33, F34, F37, F38, F39, F43, F44, F46, F48, F49, F50, F51, F54, F55, F58, F59, F60 |
Vote | 95.67% | 10 | F3, F4, F5, F7, F8, F9, F11, F12, F13, F14 |
vehicle | 73.4% | 11 | F1, F2, F3, F5, F6, F7, F9, F10, F14, F15, F18 |
HeartEW | 86.67% | 7 | F3, F7, F9, F10, F11, F12, F13 |
Datasets | BSSA | BGWO | BWOA | The Proposed Method | ||||
---|---|---|---|---|---|---|---|---|
Avg. Fit. (%) | Avg. No. F. | Avg. Fit. (%) | Avg. No. F. | Avg. Fit. (%) | Avg. No. F. | Avg. Fit. (%) | Avg. No. F. | |
BreastEW | 95.04 | 18.75 | 95.09 | 20.35 | 95.05 | 12.65 | 95.23 | 19.4 |
WaveformEW | 82.85 | 32.4 | 83.21 | 31.2 | 82.91 | 33.55 | 83.35 | 28.7 |
Sonar | 86.01 | 40.65 | 81.92 | 40.15 | 87 | 34.5 | 86.41 | 40.56 |
Vote | 95 | 9.5 | 95.38 | 9.45 | 95.13 | 7 | 95.66 | 10 |
vehicle | 66.76 | 11.65 | 68.3 | 11.4 | 67.66 | 8.4 | 68.9 | 11.63 |
HeartEW | 82.78 | 7.3 | 84.52 | 7.66 | 83.15 | 5.9 | 85.68 | 7.33 |
Methods | Average Accuracy (%) | ||||
---|---|---|---|---|---|
dB | 30 dB | 20 dB | 10 dB | 0 dB | |
BGWO | 97.5 | 89.94 | 78.27 | 64.95 | 53.45 |
BWOA | 99.68 | 89.59 | 77.98 | 64.6 | 53.34 |
BSSA | 96.98 | 90.03 | 77.97 | 64.59 | 53.04 |
Proposed | 99.28 | 90.44 | 78.1 | 64.76 | 53.35 |
Methods | Average Accuracy (%) | ||||
---|---|---|---|---|---|
30 dB | 20 dB | 10 dB | 0 dB | ||
BGWO | 99.85 | 92.9 | 79 | 65.4 | 54.95 |
BWOA | 99.95 | 92.05 | 79.35 | 65 | 53.85 |
BSSA | 99.8 | 92.8 | 79.2 | 63.75 | 55.55 |
Proposed | 99.9 | 92.95 | 79.4 | 65.1 | 55.6 |
Fault Location | Loads (hp) | Defect Diameters (Inches) | Samples | Classes |
---|---|---|---|---|
Normal | 0 | 100 | 1 | |
Inner race | 0 | 0.007 | 100 | 2 |
0.014 | 100 | 3 | ||
0.021 | 100 | 4 | ||
Ball | 0 | 0.007 | 100 | 5 |
0.014 | 100 | 6 | ||
0.021 | 100 | 7 | ||
Outer race | 0 | 0.007 | 100 | 8 |
0.014 | 100 | 9 | ||
0.021 | 100 | 10 |
Models | Classes | Accuracy (%) | Public Years |
---|---|---|---|
RWS [28] | 10 | 99.9 | 2023 |
ISVM-BT [29] | 10 | 99.9 | 2020 |
FMCNN [30] | 10 | 98.8 | 2019 |
BSSA-ER-KNN | 10 | 99.5 | |
BSSA-ER-SVM | 10 | 99.6 |
Fault Location | Load (Pound) | Samples | Classes |
---|---|---|---|
Baseline | 270 | 200 | 1 |
Inner race | 0/50/100/150/200/250/300 | 350 | 2 |
Outer race | 0/50/100/150/200/250/300 | 350 | 3 |
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Lee, C.-Y.; Le, T.-A.; Chen, Y.-C.; Hsu, S.-C. Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis. Mathematics 2024, 12, 1718. https://doi.org/10.3390/math12111718
Lee C-Y, Le T-A, Chen Y-C, Hsu S-C. Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis. Mathematics. 2024; 12(11):1718. https://doi.org/10.3390/math12111718
Chicago/Turabian StyleLee, Chun-Yao, Truong-An Le, Yung-Chi Chen, and Shih-Che Hsu. 2024. "Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis" Mathematics 12, no. 11: 1718. https://doi.org/10.3390/math12111718
APA StyleLee, C.-Y., Le, T.-A., Chen, Y.-C., & Hsu, S.-C. (2024). Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis. Mathematics, 12(11), 1718. https://doi.org/10.3390/math12111718