Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers
Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/7/1884 |
| _version_ | 1797626396843966464 |
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| author | Rafia Nishat Toma Alexander E. Prosvirin Jong-Myon Kim |
| author_facet | Rafia Nishat Toma Alexander E. Prosvirin Jong-Myon Kim |
| author_sort | Rafia Nishat Toma |
| collection | DOAJ |
| description | Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults. |
| first_indexed | 2024-03-11T10:09:50Z |
| format | Article |
| id | doaj.art-641c12e0819a4d5eb3eb1dab82f849e9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T10:09:50Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-641c12e0819a4d5eb3eb1dab82f849e92023-11-16T14:34:17ZengMDPI AGSensors1424-82202020-03-01207188410.3390/s20071884Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning ClassifiersRafia Nishat Toma0Alexander E. Prosvirin1Jong-Myon Kim2School of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, KoreaSchool of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, KoreaSchool of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, KoreaEfficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults.https://www.mdpi.com/1424-8220/20/7/1884bearing fault diagnosiscondition monitoringdecision treegenetic algorithminduction motorsKNN |
| spellingShingle | Rafia Nishat Toma Alexander E. Prosvirin Jong-Myon Kim Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers Sensors bearing fault diagnosis condition monitoring decision tree genetic algorithm induction motors KNN |
| title | Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers |
| title_full | Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers |
| title_fullStr | Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers |
| title_full_unstemmed | Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers |
| title_short | Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers |
| title_sort | bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers |
| topic | bearing fault diagnosis condition monitoring decision tree genetic algorithm induction motors KNN |
| url | https://www.mdpi.com/1424-8220/20/7/1884 |
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