Advancing Fault Diagnosis for Parallel Misalignment Detection in Induction Motors Based on Convolutional Neural Networks
Maintenance of machines is highly necessary to prolong the operational lifespan of induction motors. Prioritizing preventive measures is crucial in order to prevent more significant damage to the machinery. One of these measures includes detecting abnormalities, such as misalignment, in the motor sh...
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| Format: | Article |
| Language: | English |
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Departement of Electrical Engineering, Faculty of Engineering, Universitas Brawijaya
2023-09-01
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| Series: | Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) |
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| Online Access: | https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/1655 |
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| author | Hanif Adi Rahmawan Bambang Lelono Widjianto Katherin Indriawati Rizki Mendung Ariefianto |
| author_facet | Hanif Adi Rahmawan Bambang Lelono Widjianto Katherin Indriawati Rizki Mendung Ariefianto |
| author_sort | Hanif Adi Rahmawan |
| collection | DOAJ |
| description | Maintenance of machines is highly necessary to prolong the operational lifespan of induction motors. Prioritizing preventive measures is crucial in order to prevent more significant damage to the machinery. One of these measures includes detecting abnormalities, such as misalignment, in the motor shaft. This research is aimed to detect the misalignment of induction motor experimentally by varying the coupling between normal and parallel misalignment. The signal readings were analyzed in the frequency domain using Fast Fourier Transform (FFT). The results revealed that in the case of coupling misalignment, a peak appeared at f = 13.5 Hz, whereas in the parallel misalignment condition with a 1 cm misalignment, a peak was found at f+fr = 20 Hz. By utilizing the Convolutional Neural Network (CNN) system, normal and parallel conditions can be detected with an accuracy level of 87.5%. |
| format | Article |
| id | doaj-art-b09d0ac1caea407bb56ba8e294c00ac0 |
| institution | Kabale University |
| issn | 2460-8122 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Departement of Electrical Engineering, Faculty of Engineering, Universitas Brawijaya |
| record_format | Article |
| series | Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) |
| spelling | doaj-art-b09d0ac1caea407bb56ba8e294c00ac02024-12-14T10:28:29ZengDepartement of Electrical Engineering, Faculty of Engineering, Universitas BrawijayaJurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems)2460-81222023-09-01172667110.21776/jeeccis.v17i2.16552088Advancing Fault Diagnosis for Parallel Misalignment Detection in Induction Motors Based on Convolutional Neural NetworksHanif Adi Rahmawan0Bambang Lelono Widjianto1Katherin Indriawati2Rizki Mendung Ariefianto3Institut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberUniversitas BrawijayaMaintenance of machines is highly necessary to prolong the operational lifespan of induction motors. Prioritizing preventive measures is crucial in order to prevent more significant damage to the machinery. One of these measures includes detecting abnormalities, such as misalignment, in the motor shaft. This research is aimed to detect the misalignment of induction motor experimentally by varying the coupling between normal and parallel misalignment. The signal readings were analyzed in the frequency domain using Fast Fourier Transform (FFT). The results revealed that in the case of coupling misalignment, a peak appeared at f = 13.5 Hz, whereas in the parallel misalignment condition with a 1 cm misalignment, a peak was found at f+fr = 20 Hz. By utilizing the Convolutional Neural Network (CNN) system, normal and parallel conditions can be detected with an accuracy level of 87.5%.https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/1655induction motormisalignmentfftcnn |
| spellingShingle | Hanif Adi Rahmawan Bambang Lelono Widjianto Katherin Indriawati Rizki Mendung Ariefianto Advancing Fault Diagnosis for Parallel Misalignment Detection in Induction Motors Based on Convolutional Neural Networks Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) induction motor misalignment fft cnn |
| title | Advancing Fault Diagnosis for Parallel Misalignment Detection in Induction Motors Based on Convolutional Neural Networks |
| title_full | Advancing Fault Diagnosis for Parallel Misalignment Detection in Induction Motors Based on Convolutional Neural Networks |
| title_fullStr | Advancing Fault Diagnosis for Parallel Misalignment Detection in Induction Motors Based on Convolutional Neural Networks |
| title_full_unstemmed | Advancing Fault Diagnosis for Parallel Misalignment Detection in Induction Motors Based on Convolutional Neural Networks |
| title_short | Advancing Fault Diagnosis for Parallel Misalignment Detection in Induction Motors Based on Convolutional Neural Networks |
| title_sort | advancing fault diagnosis for parallel misalignment detection in induction motors based on convolutional neural networks |
| topic | induction motor misalignment fft cnn |
| url | https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/1655 |
| work_keys_str_mv | AT hanifadirahmawan advancingfaultdiagnosisforparallelmisalignmentdetectionininductionmotorsbasedonconvolutionalneuralnetworks AT bambanglelonowidjianto advancingfaultdiagnosisforparallelmisalignmentdetectionininductionmotorsbasedonconvolutionalneuralnetworks AT katherinindriawati advancingfaultdiagnosisforparallelmisalignmentdetectionininductionmotorsbasedonconvolutionalneuralnetworks AT rizkimendungariefianto advancingfaultdiagnosisforparallelmisalignmentdetectionininductionmotorsbasedonconvolutionalneuralnetworks |