Data-Driven Bearing Fault Diagnosis for Induction Motor
Bearings are critical components in modern manufacturing, yet they are prone to failures in induction machines. Detecting these faults early can reduce repair costs. To achieve efficient and accurate fault detection, we explore vibration-based analysis. Traditional methods rely on manual feature ext...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/7173989 |
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| author | Aqib Raqeeb Fahim Shah Zaheer Alam Subhashree Choudhury Bilal Khan R. Palanisamy |
| author_facet | Aqib Raqeeb Fahim Shah Zaheer Alam Subhashree Choudhury Bilal Khan R. Palanisamy |
| author_sort | Aqib Raqeeb |
| collection | DOAJ |
| description | Bearings are critical components in modern manufacturing, yet they are prone to failures in induction machines. Detecting these faults early can reduce repair costs. To achieve efficient and accurate fault detection, we explore vibration-based analysis. Traditional methods rely on manual feature extraction, which is time-consuming. In contrast, our work leverages deep learning, particularly convolutional neural networks, to automatically extract fault features from raw data. We investigate various image sizes (16 × 16, 32 × 32, 64 × 64, 128 × 128, 256 × 256) and their performance in bearing fault diagnosis. Our convolutional neural networks-based approach is compared to traditional methods such as support vector machine, nearest neighbors, and artificial neural networks. Results demonstrate the superior performance of our data-driven fault diagnosis using convolutional neural networks. |
| format | Article |
| id | doaj-art-fb88cc49356a497d9decc9467aa50896 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-fb88cc49356a497d9decc9467aa508962025-02-03T01:29:36ZengWileyJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/7173989Data-Driven Bearing Fault Diagnosis for Induction MotorAqib Raqeeb0Fahim Shah1Zaheer Alam2Subhashree Choudhury3Bilal Khan4R. Palanisamy5COMSATS University IslamabadUniversity of Engineering and Technology PeshawarCOMSATS University IslamabadDepartment of EEECOMSATS University IslamabadDepartment of EEEBearings are critical components in modern manufacturing, yet they are prone to failures in induction machines. Detecting these faults early can reduce repair costs. To achieve efficient and accurate fault detection, we explore vibration-based analysis. Traditional methods rely on manual feature extraction, which is time-consuming. In contrast, our work leverages deep learning, particularly convolutional neural networks, to automatically extract fault features from raw data. We investigate various image sizes (16 × 16, 32 × 32, 64 × 64, 128 × 128, 256 × 256) and their performance in bearing fault diagnosis. Our convolutional neural networks-based approach is compared to traditional methods such as support vector machine, nearest neighbors, and artificial neural networks. Results demonstrate the superior performance of our data-driven fault diagnosis using convolutional neural networks.http://dx.doi.org/10.1155/2023/7173989 |
| spellingShingle | Aqib Raqeeb Fahim Shah Zaheer Alam Subhashree Choudhury Bilal Khan R. Palanisamy Data-Driven Bearing Fault Diagnosis for Induction Motor Journal of Electrical and Computer Engineering |
| title | Data-Driven Bearing Fault Diagnosis for Induction Motor |
| title_full | Data-Driven Bearing Fault Diagnosis for Induction Motor |
| title_fullStr | Data-Driven Bearing Fault Diagnosis for Induction Motor |
| title_full_unstemmed | Data-Driven Bearing Fault Diagnosis for Induction Motor |
| title_short | Data-Driven Bearing Fault Diagnosis for Induction Motor |
| title_sort | data driven bearing fault diagnosis for induction motor |
| url | http://dx.doi.org/10.1155/2023/7173989 |
| work_keys_str_mv | AT aqibraqeeb datadrivenbearingfaultdiagnosisforinductionmotor AT fahimshah datadrivenbearingfaultdiagnosisforinductionmotor AT zaheeralam datadrivenbearingfaultdiagnosisforinductionmotor AT subhashreechoudhury datadrivenbearingfaultdiagnosisforinductionmotor AT bilalkhan datadrivenbearingfaultdiagnosisforinductionmotor AT rpalanisamy datadrivenbearingfaultdiagnosisforinductionmotor |