Detection of Inter Turn Short Circuit Faults in Induction Motor Using Artificial Neural Network
AbstractThis paper proposes a new method using Artificial Neural Network (ANN) for detection of different Inter Turn Short Circuit (ITSC) faults in an induction motor under different loading conditions. The stator current signal was obtained experimentally from a healthy motor and a faulty motor wit...
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| Format: | Article |
| Language: | English |
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FRUCT
2020-04-01
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| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://www.fruct.org/publications/fruct26/files/Moh.pdf |
| _version_ | 1818210005744091136 |
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| author | Menshawy A. Mohamed Essam Mohamed Al-Attar A. Mohamed Mohamed M. Abdel-Nasser Mohamed A. Moustafa Hassan |
| author_facet | Menshawy A. Mohamed Essam Mohamed Al-Attar A. Mohamed Mohamed M. Abdel-Nasser Mohamed A. Moustafa Hassan |
| author_sort | Menshawy A. Mohamed |
| collection | DOAJ |
| description | AbstractThis paper proposes a new method using Artificial Neural Network (ANN) for detection of different Inter Turn Short Circuit (ITSC) faults in an induction motor under different loading conditions. The stator current signal was obtained experimentally from a healthy motor and a faulty motor with ITSC faults. The statistical time domain features was extracted from stator current signal, these features are used to train and test an ANN in order to diagnose ITSC faults. A complete study is performed by considering various diagnosis methods from ANN and machine learning algorithms, including Decision Tree (DT), K-Nearest Neighbours (KNN), Naive Bayes (NB), Random Forest (RF) and Support Vector Machine (SVM) for diagnosis ITSC faults. The performance of the proposed method was compared with machine learning algorithms, the proposed method has a higher accuracy than the other algorithms. Trained neural networks are able to classify different states of the ITSC faults with satisfied accuracy. The efficiency of this approach has been proven using experimental tests to diagnose ITCS faults in a 1.5Hp squirrel cage induction motor. |
| first_indexed | 2024-12-12T05:09:44Z |
| format | Article |
| id | doaj.art-4467a2a514cd4a6a8c72d5b9253a5bc1 |
| institution | Directory Open Access Journal |
| issn | 2305-7254 2343-0737 |
| language | English |
| last_indexed | 2024-12-12T05:09:44Z |
| publishDate | 2020-04-01 |
| publisher | FRUCT |
| record_format | Article |
| series | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| spelling | doaj.art-4467a2a514cd4a6a8c72d5b9253a5bc12022-12-22T00:36:58ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372020-04-0126129730410.23919/FRUCT48808.2020.9087535Detection of Inter Turn Short Circuit Faults in Induction Motor Using Artificial Neural NetworkMenshawy A. Mohamed0Essam Mohamed1Al-Attar A. Mohamed2Mohamed M. Abdel-Nasser3Mohamed A. Moustafa Hassan4Qena Water and Wastewater Company1, EgyptS. Valley University; Qena / Egypt /, EgyptAswan University; Egypt, EgyptAswan University; Egypt, EgyptCairo University, EgyptAbstractThis paper proposes a new method using Artificial Neural Network (ANN) for detection of different Inter Turn Short Circuit (ITSC) faults in an induction motor under different loading conditions. The stator current signal was obtained experimentally from a healthy motor and a faulty motor with ITSC faults. The statistical time domain features was extracted from stator current signal, these features are used to train and test an ANN in order to diagnose ITSC faults. A complete study is performed by considering various diagnosis methods from ANN and machine learning algorithms, including Decision Tree (DT), K-Nearest Neighbours (KNN), Naive Bayes (NB), Random Forest (RF) and Support Vector Machine (SVM) for diagnosis ITSC faults. The performance of the proposed method was compared with machine learning algorithms, the proposed method has a higher accuracy than the other algorithms. Trained neural networks are able to classify different states of the ITSC faults with satisfied accuracy. The efficiency of this approach has been proven using experimental tests to diagnose ITCS faults in a 1.5Hp squirrel cage induction motor.https://www.fruct.org/publications/fruct26/files/Moh.pdfartificial neural networkdetection of inter turn short circuit faultsinduction motordata pre-processing |
| spellingShingle | Menshawy A. Mohamed Essam Mohamed Al-Attar A. Mohamed Mohamed M. Abdel-Nasser Mohamed A. Moustafa Hassan Detection of Inter Turn Short Circuit Faults in Induction Motor Using Artificial Neural Network Proceedings of the XXth Conference of Open Innovations Association FRUCT artificial neural network detection of inter turn short circuit faults induction motor data pre-processing |
| title | Detection of Inter Turn Short Circuit Faults in Induction Motor Using Artificial Neural Network |
| title_full | Detection of Inter Turn Short Circuit Faults in Induction Motor Using Artificial Neural Network |
| title_fullStr | Detection of Inter Turn Short Circuit Faults in Induction Motor Using Artificial Neural Network |
| title_full_unstemmed | Detection of Inter Turn Short Circuit Faults in Induction Motor Using Artificial Neural Network |
| title_short | Detection of Inter Turn Short Circuit Faults in Induction Motor Using Artificial Neural Network |
| title_sort | detection of inter turn short circuit faults in induction motor using artificial neural network |
| topic | artificial neural network detection of inter turn short circuit faults induction motor data pre-processing |
| url | https://www.fruct.org/publications/fruct26/files/Moh.pdf |
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