Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification
Abstract
:1. Introduction
2. Related Methods
2.1. Feature Selection Methods
2.2. Classifiers
3. Proposed Method
3.1. Fusion Multi-Filter Feature Selection
3.2. Exhaustive Search Application
4. Case Studies
4.1. Data Collection
4.2. Feature Extraction and Selection
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Frequency Domain Features | Time Domain Features | ||||||
---|---|---|---|---|---|---|---|
abs_mean_F | 0 | 24 | 48 | abs_mean_T | 12 | 36 | 60 |
peak_m_F | 1 | 25 | 49 | peak_m_T | 13 | 37 | 61 |
kur_F | 2 | 26 | 50 | kur_T | 14 | 38 | 62 |
skew_F | 3 | 27 | 51 | skew_T | 15 | 39 | 63 |
rms_F | 4 | 28 | 52 | rms_T | 16 | 40 | 64 |
mean_F | 5 | 29 | 53 | mean_T | 17 | 41 | 65 |
std_F | 6 | 30 | 54 | std_T | 18 | 42 | 66 |
min_F | 7 | 31 | 55 | min_T | 19 | 43 | 67 |
25%_F | 8 | 32 | 56 | 25%_T | 20 | 44 | 68 |
50%_F | 9 | 33 | 57 | 50%_T | 21 | 45 | 69 |
75%_F | 10 | 34 | 58 | 75%_T | 22 | 46 | 70 |
max_F | 11 | 35 | 59 | max_T | 23 | 47 | 71 |
Conditions | EEV | Fan Speed (rev/min) | Frequency (Hz) |
---|---|---|---|
Cooling | 60, 120, 180, 240, 300, 360 | 350, 500, 700 | 20, 30, 40, 50 |
Heating | 60, 120, 180, 240, 300, 360 | 350, 500, 700 | 20, 30, 40, 50 |
Conditions | Refrigerant (%) | Frequency (Hz) |
---|---|---|
Normal | 100 | 30~90 |
Abnormal | 50~90 | 30~90 |
Cases | Stages | Features | No. of Features |
---|---|---|---|
Case 1 | Multi-filter clustering | {25%_T} ∪ {max_F, mean_T} ∪ {ptp_F, 75%_T, abs_mean_F, rms_F, mean_F, std_F, 25%_F, 50%_F, 75%_F, max_F, abs_mean_T, rms_T, std_T, 25%_T, 75%_T, skew_F} | 19 |
Fusion | 25%_T, max_F, mean_T, ptp_F, 75%_T, abs_mean_F, rms_F, mean_F, std_F, 25%_F, 50%_F, 75%_F, abs_mean_T, rms_T, std_T, 75%_T, skew_F | 17 | |
Final set | SVM: rms_T, 75%_T, KNN: mean_T, 75%_T, mean_F, MLP: mean_T, 75%_T, ptp_F | 2,3,3 | |
Case 2 | Multi-filter clustering | {kur_T} ∪ {kur_F, skew_F, mean_T} ∪ {kur_F, skew_F, mean_T, ptp_F, std_F, max_F, kur_T, skew_T, min_T, 50%_T} | 14 |
Fusion | kur_T, kur_F, skew_F, mean_T, ptp_F, std_F, max_F, skew_T, min_T, 50%_T | 10 | |
Final set | SVM: skew_F, std_F, kur_T, skew_T, KNN: kur_T, skew_F, std_F, skew_T, MLP: kur_F, max_F, skew_T, mean_T | 4,4,4 | |
Case 3 | Multi-filter clustering | {ptp_T, kur_T} ∪ {kur_T, min_T} ∪ {ptp_F, ptp_T, kur_T, min_T, max_T} | 9 |
Fusion | ptp_T, kur_T, min_T, ptp_F, max_T | 5 | |
Final set | SVM: ptp_T, kur_T, ptp_F, min_T, KNN: ptp_T, kur_T, ptp_F, min_T MLP: kur_T, ptp_F, min_T, max_T | 4,4,4 | |
Case 4 | Multi-filter clustering | {75%_F} ∪ {75%_F, 50%_F, rms_F, abs_mean_F, std_F} ∪ {75%_F, rms_F, mean_F, 50%_T} | 10 |
Fusion | 75%_F, 50%_F, rms_F abs_mean_F, mean_F, std_F, 50%_T | 7 | |
Final set | SVM: abs_mean_F, rms_F, mean_F, KNN: abs_mean_F, mean_F, std_F, MLP: 75%_F, abs_mean_F, mean_F, std_F | 3,3,4 |
Methods | Case 1 | Case 2 | Case 3 | Case 4 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SVM | KNN | MLP | SVM | KNN | MLP | SVM | KNN | MLP | SVM | KNN | MLP | Avg. | ||
Accuracy | CS | 0.93 | 0.99 | 0.93 | 0.91 | 0.95 | 0.92 | 0.97 | 0.99 | 0.96 | 0.76 | 0.94 | 0.95 | 0.93 |
ETC | 0.98 | 0.99 | 0.96 | 0.88 | 0.95 | 0.93 | 0.98 | 0.99 | 0.97 | 0.86 | 0.99 | 0.96 | 0.95 | |
CM | 0.93 | 0.98 | 0.93 | 0.93 | 0.97 | 0.98 | 0.98 | 0.99 | 0.98 | 0.94 | 0.98 | 0.96 | 0.96 | |
MFCF | 1.0 | 1.0 | 1.0 | 0.99 | 1.0 | 0.99 | 0.99 | 0.99 | 0.99 | 1.0 | 1.0 | 1.0 | 0.99 | |
Efficiency (sec.) | CS | 3.52 | 114.9 | 54.2 | 6.75 | 104.2 | 47.62 | 90.2 | 659.5 | 80.94 | 0.4 | 30.3 | 11.4 | 100.3 |
ETC | 3.01 | 115.4 | 23.9 | 5.23 | 103.6 | 52.7 | 84.1 | 678.9 | 70.89 | 0.4 | 30.8 | 13.4 | 98.5 | |
CM | 3.5 | 118.6 | 43.3 | 4.99 | 100.9 | 36.6 | 86.5 | 680.8 | 69.27 | 0.4 | 30.5 | 9.1 | 98.7 | |
MFCF | 2.4 | 113.2 | 13.8 | 4.02 | 100.1 | 39.6 | 83.0 | 655.3 | 81.63 | 0.4 | 30.0 | 10.0 | 94.4 |
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Mochammad, S.; Noh, Y.; Kang, Y.-J.; Park, S.; Lee, J.; Chin, S. Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification. Sensors 2022, 22, 2192. https://doi.org/10.3390/s22062192
Mochammad S, Noh Y, Kang Y-J, Park S, Lee J, Chin S. Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification. Sensors. 2022; 22(6):2192. https://doi.org/10.3390/s22062192
Chicago/Turabian StyleMochammad, Solichin, Yoojeong Noh, Young-Jin Kang, Sunhwa Park, Jangwoo Lee, and Simon Chin. 2022. "Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification" Sensors 22, no. 6: 2192. https://doi.org/10.3390/s22062192
APA StyleMochammad, S., Noh, Y., Kang, Y.-J., Park, S., Lee, J., & Chin, S. (2022). Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification. Sensors, 22(6), 2192. https://doi.org/10.3390/s22062192