Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction
This paper proposes an Adaptive Neural Fuzzy Inference System (ANFIS) model for diagnosis of combined Inter Turn Short Circuit (ITSC) and Broken Rotor Bar (BRB) faults in a Squirrel Cage Induction Motor (SC-IM). The signal of the stator current is obtained from a really healthy and faulty SC-IM. Exp...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/19/9115 |
| _version_ | 1797516765563977728 |
|---|---|
| author | Menshawy A. Mohamed Mohamed A. Moustafa Hassan Fahad Albalawi Sherif S. M. Ghoneim Ziad M. Ali Mostafa Dardeer |
| author_facet | Menshawy A. Mohamed Mohamed A. Moustafa Hassan Fahad Albalawi Sherif S. M. Ghoneim Ziad M. Ali Mostafa Dardeer |
| author_sort | Menshawy A. Mohamed |
| collection | DOAJ |
| description | This paper proposes an Adaptive Neural Fuzzy Inference System (ANFIS) model for diagnosis of combined Inter Turn Short Circuit (ITSC) and Broken Rotor Bar (BRB) faults in a Squirrel Cage Induction Motor (SC-IM). The signal of the stator current is obtained from a really healthy and faulty SC-IM. Experimental tests have been set up using a 1.5 Hp/380 V three-phase SC-IM with different combined ITSC and BRB faults under different loading conditions. Before entering the model, the Discrete Wavelet Transform (DWT) pre-processes the stator current signal. The DWT generates data sets in order to evaluate the proposed technique. ANFIS based on DWT is used successfully to diagnose the most relevant faults very effectively. In addition, ANFIS based on the DWT method has been compared to ANFIS and ANFIS based on an auto-regressive model, finding that the proposed method achieves higher efficiency than the previous one. The proposed ANFIS based on the DWT model classifies entirely different states of combined ITSC and BRB faults with high accuracy. |
| first_indexed | 2024-03-10T07:05:27Z |
| format | Article |
| id | doaj.art-1899b474a0d540998ddf3b4542eee536 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T07:05:27Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1899b474a0d540998ddf3b4542eee5362023-11-22T15:48:07ZengMDPI AGApplied Sciences2076-34172021-09-011119911510.3390/app11199115Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature ExtractionMenshawy A. Mohamed0Mohamed A. Moustafa Hassan1Fahad Albalawi2Sherif S. M. Ghoneim3Ziad M. Ali4Mostafa Dardeer5Electric Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, EgyptElectric Power Department, Faculty of Engineering, Cairo University, Giza 12631, EgyptDepartment of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaElectric Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, EgyptElectric Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, EgyptThis paper proposes an Adaptive Neural Fuzzy Inference System (ANFIS) model for diagnosis of combined Inter Turn Short Circuit (ITSC) and Broken Rotor Bar (BRB) faults in a Squirrel Cage Induction Motor (SC-IM). The signal of the stator current is obtained from a really healthy and faulty SC-IM. Experimental tests have been set up using a 1.5 Hp/380 V three-phase SC-IM with different combined ITSC and BRB faults under different loading conditions. Before entering the model, the Discrete Wavelet Transform (DWT) pre-processes the stator current signal. The DWT generates data sets in order to evaluate the proposed technique. ANFIS based on DWT is used successfully to diagnose the most relevant faults very effectively. In addition, ANFIS based on the DWT method has been compared to ANFIS and ANFIS based on an auto-regressive model, finding that the proposed method achieves higher efficiency than the previous one. The proposed ANFIS based on the DWT model classifies entirely different states of combined ITSC and BRB faults with high accuracy.https://www.mdpi.com/2076-3417/11/19/9115ANFISbroken rotor bar faultDWTfeature extractioninter turn short circuit fault |
| spellingShingle | Menshawy A. Mohamed Mohamed A. Moustafa Hassan Fahad Albalawi Sherif S. M. Ghoneim Ziad M. Ali Mostafa Dardeer Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction Applied Sciences ANFIS broken rotor bar fault DWT feature extraction inter turn short circuit fault |
| title | Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction |
| title_full | Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction |
| title_fullStr | Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction |
| title_full_unstemmed | Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction |
| title_short | Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction |
| title_sort | diagnostic modelling for induction motor faults via anfis algorithm and dwt based feature extraction |
| topic | ANFIS broken rotor bar fault DWT feature extraction inter turn short circuit fault |
| url | https://www.mdpi.com/2076-3417/11/19/9115 |
| work_keys_str_mv | AT menshawyamohamed diagnosticmodellingforinductionmotorfaultsviaanfisalgorithmanddwtbasedfeatureextraction AT mohamedamoustafahassan diagnosticmodellingforinductionmotorfaultsviaanfisalgorithmanddwtbasedfeatureextraction AT fahadalbalawi diagnosticmodellingforinductionmotorfaultsviaanfisalgorithmanddwtbasedfeatureextraction AT sherifsmghoneim diagnosticmodellingforinductionmotorfaultsviaanfisalgorithmanddwtbasedfeatureextraction AT ziadmali diagnosticmodellingforinductionmotorfaultsviaanfisalgorithmanddwtbasedfeatureextraction AT mostafadardeer diagnosticmodellingforinductionmotorfaultsviaanfisalgorithmanddwtbasedfeatureextraction |