Implementation of Support Vector Machine Algorithm in a Real-time BLDC Motor Bearing Fault Classification with Discrete Wavelet Transform as Feature Extractor
Brushless DC (BLDC) Motors are integral to industrial operations. Continuous motor usage can lead to various faults with significant consequences if left unaddressed. These faults may impact the motor, its surrounding system, disrupt economic activities, and potentially result in catastrophic failur...
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| Vydáno v: | 2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) s. 1 - 6 |
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IEEE
26.08.2024
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| Abstract | Brushless DC (BLDC) Motors are integral to industrial operations. Continuous motor usage can lead to various faults with significant consequences if left unaddressed. These faults may impact the motor, its surrounding system, disrupt economic activities, and potentially result in catastrophic failures. This study introduces a methodology combining Support Vector Machine (SVM) for feature classification with Discrete Wavelet Transform (DWT) for feature extraction. Through machine learning techniques, voltage signals from multiple BLDC motor samples with diverse faults were examined. Performance metrics, including precision, recall, accuracy, and F-1 scores, were calculated to evaluate the algorithm's effectiveness. The Support Vector Machine, trained alongside the Discrete Wavelet Transform, achieved an accuracy of 96.98 percent during validation and 90.37 percent during real-time testing. These results highlight the practical application of the proposed algorithm for efficient motor fault diagnosis. |
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| AbstractList | Brushless DC (BLDC) Motors are integral to industrial operations. Continuous motor usage can lead to various faults with significant consequences if left unaddressed. These faults may impact the motor, its surrounding system, disrupt economic activities, and potentially result in catastrophic failures. This study introduces a methodology combining Support Vector Machine (SVM) for feature classification with Discrete Wavelet Transform (DWT) for feature extraction. Through machine learning techniques, voltage signals from multiple BLDC motor samples with diverse faults were examined. Performance metrics, including precision, recall, accuracy, and F-1 scores, were calculated to evaluate the algorithm's effectiveness. The Support Vector Machine, trained alongside the Discrete Wavelet Transform, achieved an accuracy of 96.98 percent during validation and 90.37 percent during real-time testing. These results highlight the practical application of the proposed algorithm for efficient motor fault diagnosis. |
| Author | Reyes, Ivan Ross M. Ostia, Conrado F. Masangkay, Shan Joshua Raym C. Mendigoria, Lester Joseph B. |
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| Snippet | Brushless DC (BLDC) Motors are integral to industrial operations. Continuous motor usage can lead to various faults with significant consequences if left... |
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| SubjectTerms | Accuracy Arduino Programming Brushless DC Motor Classification algorithms Discrete Wavelet Transform Discrete wavelet transforms Fault diagnosis Feature Classification Feature extraction Haar wavelet MATLAB Simulink Motor Fault Diagnosis Motors Real-time bearing fault diagnosis Real-time systems Support Vector Machine Support vector machines Testing Wavelet analysis |
| Title | Implementation of Support Vector Machine Algorithm in a Real-time BLDC Motor Bearing Fault Classification with Discrete Wavelet Transform as Feature Extractor |
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