AI-driven thermography-based fault diagnosis in single-phase induction motor
Single-phase induction motors (SIMs) are commonly used in industrial applications. The extensive industrial usage of SIMs requires proper maintenance and fault detection. Among various faults, the most common mechanical faults in SIMs are bearing faults. Thus, detecting these faults during motor ope...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024017456 |
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| author | Muhammad Atif Shoaib Azmat Faisal Khan Fahad R. Albogamy Adam Khan |
| author_facet | Muhammad Atif Shoaib Azmat Faisal Khan Fahad R. Albogamy Adam Khan |
| author_sort | Muhammad Atif |
| collection | DOAJ |
| description | Single-phase induction motors (SIMs) are commonly used in industrial applications. The extensive industrial usage of SIMs requires proper maintenance and fault detection. Among various faults, the most common mechanical faults in SIMs are bearing faults. Thus, detecting these faults during motor operation is essential for preventing damage. Many fault diagnosis methods have been proposed to detect bearing faults based on contact sensors, but they have accessibility problems. This paper presents a novel Infrared Thermography (IRT) based fault diagnosis method, leveraging both conventional machine learning (CML) and deep learning (DL) techniques for motor condition classification. In CML, Statistical and Gray Level Co-occurrence Matrix (GLCM) features are extracted from thermal images. The support vector machine recursive feature elimination (SVM-RFE) method is used to select the most relevant and highest-score features from the extracted features. Support vector machine (SVM) with linear and radial basis function (RBF) kernel functions and K-nearest neighbours (KNN) classifiers are applied to the selected interpretable features to classify machines into five classes of healthy, inner race, outer race, missing ball with low lubrication, and no lubrication bearing faults. Furthermore, in DL, a convolutional neural network (CNN) of few simple convolutional layers with several filters is also used as a healthy and faulty bearings classifier. The proposed method achieves classification accuracy of 98.29 % using CML and 100 % using DL. Moreover, our IRT-based SIMs bearing fault diagnosis methods demonstrate a reduction in architectural complexity compared to prevailing state-of-the-art approaches. |
| format | Article |
| id | doaj-art-24f3fdbdfdc34a7f9446f8e2be035c83 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-24f3fdbdfdc34a7f9446f8e2be035c832025-08-20T02:52:27ZengElsevierResults in Engineering2590-12302024-12-012410349310.1016/j.rineng.2024.103493AI-driven thermography-based fault diagnosis in single-phase induction motorMuhammad Atif0Shoaib Azmat1Faisal Khan2Fahad R. Albogamy3Adam Khan4Electrical and Computer Engineering, COMSATS, Abbottabad 22060, PakistanElectrical and Computer Engineering, COMSATS, Abbottabad 22060, PakistanElectrical and Computer Engineering, COMSATS, Abbottabad 22060, Pakistan; Corresponding author.Electronics Engineering, UET, Abbottabad 22010, PakistanElectronics Engineering, UET, Abbottabad 22010, PakistanSingle-phase induction motors (SIMs) are commonly used in industrial applications. The extensive industrial usage of SIMs requires proper maintenance and fault detection. Among various faults, the most common mechanical faults in SIMs are bearing faults. Thus, detecting these faults during motor operation is essential for preventing damage. Many fault diagnosis methods have been proposed to detect bearing faults based on contact sensors, but they have accessibility problems. This paper presents a novel Infrared Thermography (IRT) based fault diagnosis method, leveraging both conventional machine learning (CML) and deep learning (DL) techniques for motor condition classification. In CML, Statistical and Gray Level Co-occurrence Matrix (GLCM) features are extracted from thermal images. The support vector machine recursive feature elimination (SVM-RFE) method is used to select the most relevant and highest-score features from the extracted features. Support vector machine (SVM) with linear and radial basis function (RBF) kernel functions and K-nearest neighbours (KNN) classifiers are applied to the selected interpretable features to classify machines into five classes of healthy, inner race, outer race, missing ball with low lubrication, and no lubrication bearing faults. Furthermore, in DL, a convolutional neural network (CNN) of few simple convolutional layers with several filters is also used as a healthy and faulty bearings classifier. The proposed method achieves classification accuracy of 98.29 % using CML and 100 % using DL. Moreover, our IRT-based SIMs bearing fault diagnosis methods demonstrate a reduction in architectural complexity compared to prevailing state-of-the-art approaches.http://www.sciencedirect.com/science/article/pii/S2590123024017456Thermal imagesBearing faultsSingle phase induction motorFault diagnosisMachine learningCNN |
| spellingShingle | Muhammad Atif Shoaib Azmat Faisal Khan Fahad R. Albogamy Adam Khan AI-driven thermography-based fault diagnosis in single-phase induction motor Results in Engineering Thermal images Bearing faults Single phase induction motor Fault diagnosis Machine learning CNN |
| title | AI-driven thermography-based fault diagnosis in single-phase induction motor |
| title_full | AI-driven thermography-based fault diagnosis in single-phase induction motor |
| title_fullStr | AI-driven thermography-based fault diagnosis in single-phase induction motor |
| title_full_unstemmed | AI-driven thermography-based fault diagnosis in single-phase induction motor |
| title_short | AI-driven thermography-based fault diagnosis in single-phase induction motor |
| title_sort | ai driven thermography based fault diagnosis in single phase induction motor |
| topic | Thermal images Bearing faults Single phase induction motor Fault diagnosis Machine learning CNN |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024017456 |
| work_keys_str_mv | AT muhammadatif aidriventhermographybasedfaultdiagnosisinsinglephaseinductionmotor AT shoaibazmat aidriventhermographybasedfaultdiagnosisinsinglephaseinductionmotor AT faisalkhan aidriventhermographybasedfaultdiagnosisinsinglephaseinductionmotor AT fahadralbogamy aidriventhermographybasedfaultdiagnosisinsinglephaseinductionmotor AT adamkhan aidriventhermographybasedfaultdiagnosisinsinglephaseinductionmotor |