Vibration Signal Classification Using Stochastic Configuration Networks Ensemble
Abstract
:1. Introduction
2. Stochastic Configuration Networks
3. Fault Diagnosis Based on Ensemble SCNs
3.1. SCN Model with Hybrid Data
3.2. SCNs Ensemble
Algorithm 1: SCN with hybrid data |
- SCN-D:
- SCN-X:
- SCN-H:
Algorithm 2: SCNs ensemble |
4. Performance Evaluation
4.1. Experimental Setup
4.2. Results and Discussion
4.3. Comparison with Deep Learning Models
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Damage Size | Samples | Fault Mode | Label |
---|---|---|---|
∼ | 238 | Normal | 0 |
0.007 in. | 118 | Inner ring | 1 |
0.014 in. | 119 | Inner ring | 2 |
0.021 in. | 119 | Inner ring | 3 |
0.007 in. | 118 | Rolling element | 4 |
0.014 in. | 118 | Rolling element | 5 |
0.021 in. | 118 | Rolling element | 6 |
0.007 in. | 119 | Outer ring | 7 |
0.014 in. | 119 | Outer ring | 8 |
0.021 in. | 119 | Outer ring | 9 |
Dataset | Model | Accuracy | Precision | Recall |
---|---|---|---|---|
JNU | NNRW | 0.9338 | 0.8775 | 0.9887 |
NNRW2D | 0.8741 | 0.7673 | 0.9756 | |
SCN | 0.9558 | 0.9182 | 0.9929 | |
SCN2D | 0.9200 | 0.8501 | 0.9883 | |
SCN-H | 0.9660 | 0.9372 | 0.9945 | |
SEU | NNRW | 0.9490 | 0.9034 | 0.9941 |
NNRW2D | 0.9027 | 0.8154 | 0.9879 | |
SCN | 0.9786 | 0.9595 | 0.9976 | |
SCN2D | 0.9682 | 0.9397 | 0.9965 | |
SCN-H | 0.9854 | 0.9723 | 0.9984 | |
UoC | NNRW | 0.9517 | 0.9143 | 0.9882 |
NNRW2D | 0.6740 | 0.4189 | 0.8553 | |
SCN | 0.9519 | 0.9148 | 0.9881 | |
SCN2D | 0.7147 | 0.4902 | 0.8896 | |
SCN-H | 0.9582 | 0.9261 | 0.9897 |
Model | JNU | SEU | UoC | ||||||
---|---|---|---|---|---|---|---|---|---|
Accuracy | Precision | Recall | Accuracy | Precision | Recall | Accuracy | Precision | Recall | |
DNNE | 0.9466 | 0.9013 | 0.9911 | 0.9571 | 0.9186 | 0.9953 | 0.9566 | 0.9233 | 0.9893 |
SCNE | 0.9570 | 0.9203 | 0.9933 | 0.9826 | 0.9670 | 0.9981 | 0.9571 | 0.9241 | 0.9894 |
RNNE-RVFL | 0.9573 | 0.9212 | 0.9930 | 0.9863 | 0.9739 | 0.9986 | 0.9599 | 0.9289 | 0.9903 |
RNNE-SCN | 0.9659 | 0.9366 | 0.9948 | 0.9963 | 0.9930 | 0.9996 | 0.9638 | 0.9360 | 0.9912 |
SCN-Ensemble (average) | 0.9722 | 0.9485 | 0.9958 | 0.9937 | 0.9881 | 0.9993 | 0.9575 | 0.9246 | 0.9897 |
SCN-Ensemble (voting) | 0.9685 | 0.9415 | 0.9952 | 0.9834 | 0.9686 | 0.9981 | 0.9630 | 0.9345 | 0.9910 |
SCN-Ensemble (NCL) | 0.9726 | 0.9492 | 0.9960 | 0.9975 | 0.9954 | 0.9997 | 0.9621 | 0.9327 | 0.9909 |
Model | CWRU | JNU | SEU | UoC |
---|---|---|---|---|
MLP | 1.000 | 0.9488 | 0.9290 | 0.6598 |
CNN | 1.000 | 0.9352 | 0.9669 | 0.6332 |
DAE | 1.000 | 0.9446 | 0.9718 | 0.8417 |
AlexNet | 1.000 | 0.9579 | 0.9731 | 0.6722 |
ResNet18 | 1.000 | 0.9548 | 0.9804 | 0.7969 |
LSTM | 1.000 | 0.9564 | 0.9650 | 0.8174 |
SCN-H | 1.000 | 0.9660 | 0.9854 | 0.9582 |
SCN-Ensemble | 1.000 | 0.9726 | 0.9975 | 0.9621 |
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Wang, Q.; Liu, D.; Tian, H.; Qin, Y.; Zhao, D. Vibration Signal Classification Using Stochastic Configuration Networks Ensemble. Appl. Sci. 2024, 14, 5589. https://doi.org/10.3390/app14135589
Wang Q, Liu D, Tian H, Qin Y, Zhao D. Vibration Signal Classification Using Stochastic Configuration Networks Ensemble. Applied Sciences. 2024; 14(13):5589. https://doi.org/10.3390/app14135589
Chicago/Turabian StyleWang, Qinxia, Dandan Liu, Hao Tian, Yongpeng Qin, and Difei Zhao. 2024. "Vibration Signal Classification Using Stochastic Configuration Networks Ensemble" Applied Sciences 14, no. 13: 5589. https://doi.org/10.3390/app14135589
APA StyleWang, Q., Liu, D., Tian, H., Qin, Y., & Zhao, D. (2024). Vibration Signal Classification Using Stochastic Configuration Networks Ensemble. Applied Sciences, 14(13), 5589. https://doi.org/10.3390/app14135589