Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach
This paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform (WPT). The proposed algorithm takes a frame of samples from the three-phase supply current of an induction motor. The three phase current samples are then combined to generate a single current...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/8/1253 |
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| author | Ameer M. Hussein Adel A. Obed Rana H. A. Zubo Yasir I. A. Al-Yasir Ameer L. Saleh Hussein Fadhel Akbar Sheikh-Akbari Geev Mokryani Raed A. Abd-Alhameed |
| author_facet | Ameer M. Hussein Adel A. Obed Rana H. A. Zubo Yasir I. A. Al-Yasir Ameer L. Saleh Hussein Fadhel Akbar Sheikh-Akbari Geev Mokryani Raed A. Abd-Alhameed |
| author_sort | Ameer M. Hussein |
| collection | DOAJ |
| description | This paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform (WPT). The proposed algorithm takes a frame of samples from the three-phase supply current of an induction motor. The three phase current samples are then combined to generate a single current signal by computing the Root Mean Square (RMS) value of the three phase current samples at each time stamp. The resulting current samples are then divided into windows of 64 samples. Each resulting window of samples is then processed separately. The proposed algorithm uses two methods to create window samples, which are called non-overlapping window samples and moving/overlapping window samples. Non-overlapping window samples are created by simply dividing the current samples into windows of 64 samples, while the moving window samples are generated by taking the first 64 current samples, and then the consequent moving window samples are generated by moving the window across the current samples by one sample each time. The new window of samples consists of the last 63 samples of the previous window and one new sample. The overlapping method reduces the fault detection time to a single sample accuracy. However, it is computationally more expensive than the non-overlapping method and requires more computer memory. The resulting window samples are separately processed as follows: The proposed algorithm performs two level WPT on each resulting window samples, dividing its coefficients into its four wavelet subbands. Information in wavelet high frequency subbands is then used for fault detection and activating the trip signal to disconnect the motor from the power supply. The proposed algorithm was first implemented in the MATLAB platform, and the Entropy power Energy (EE) of the high frequency WPT subbands’ coefficients was used to determine the condition of the motor. If the induction motor is faulty, the algorithm proceeds to identify the type of the fault. An empirical setup of the proposed system was then implemented, and the proposed algorithm condition was tested under real, where different faults were practically induced to the induction motor. Experimental results confirmed the effectiveness of the proposed technique. To generalize the proposed method, the experiment was repeated on different types of induction motors with different working ages and with different power ratings. Experimental results show that the capability of the proposed method is independent of the types of motors used and their ages. |
| first_indexed | 2024-03-09T10:38:33Z |
| format | Article |
| id | doaj.art-4849d5945fe548b99de5fbd1be856f00 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T10:38:33Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-4849d5945fe548b99de5fbd1be856f002023-12-01T20:47:15ZengMDPI AGElectronics2079-92922022-04-01118125310.3390/electronics11081253Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy ApproachAmeer M. Hussein0Adel A. Obed1Rana H. A. Zubo2Yasir I. A. Al-Yasir3Ameer L. Saleh4Hussein Fadhel5Akbar Sheikh-Akbari6Geev Mokryani7Raed A. Abd-Alhameed8Electrical Engineering Technical College, Middle Technical University, Baghdad 10001, IraqElectrical Engineering Technical College, Middle Technical University, Baghdad 10001, IraqTechnical Engineering College Kirkuk, Northern Technical University, Kirkuk 00964, IraqBiomedical and Electronics Engineering, Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UKDepartment of Electrical Engineering, University of Misan, Misan 62001, IraqTechnical Engineering College Kirkuk, Northern Technical University, Kirkuk 00964, IraqSchool of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS6 3QR, UKBiomedical and Electronics Engineering, Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UKBiomedical and Electronics Engineering, Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UKThis paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform (WPT). The proposed algorithm takes a frame of samples from the three-phase supply current of an induction motor. The three phase current samples are then combined to generate a single current signal by computing the Root Mean Square (RMS) value of the three phase current samples at each time stamp. The resulting current samples are then divided into windows of 64 samples. Each resulting window of samples is then processed separately. The proposed algorithm uses two methods to create window samples, which are called non-overlapping window samples and moving/overlapping window samples. Non-overlapping window samples are created by simply dividing the current samples into windows of 64 samples, while the moving window samples are generated by taking the first 64 current samples, and then the consequent moving window samples are generated by moving the window across the current samples by one sample each time. The new window of samples consists of the last 63 samples of the previous window and one new sample. The overlapping method reduces the fault detection time to a single sample accuracy. However, it is computationally more expensive than the non-overlapping method and requires more computer memory. The resulting window samples are separately processed as follows: The proposed algorithm performs two level WPT on each resulting window samples, dividing its coefficients into its four wavelet subbands. Information in wavelet high frequency subbands is then used for fault detection and activating the trip signal to disconnect the motor from the power supply. The proposed algorithm was first implemented in the MATLAB platform, and the Entropy power Energy (EE) of the high frequency WPT subbands’ coefficients was used to determine the condition of the motor. If the induction motor is faulty, the algorithm proceeds to identify the type of the fault. An empirical setup of the proposed system was then implemented, and the proposed algorithm condition was tested under real, where different faults were practically induced to the induction motor. Experimental results confirmed the effectiveness of the proposed technique. To generalize the proposed method, the experiment was repeated on different types of induction motors with different working ages and with different power ratings. Experimental results show that the capability of the proposed method is independent of the types of motors used and their ages.https://www.mdpi.com/2079-9292/11/8/1253electrical fault detectionelectrical fault classificationthree-phase induction motorwavelet packet transformwavelet power energyand moving window technique |
| spellingShingle | Ameer M. Hussein Adel A. Obed Rana H. A. Zubo Yasir I. A. Al-Yasir Ameer L. Saleh Hussein Fadhel Akbar Sheikh-Akbari Geev Mokryani Raed A. Abd-Alhameed Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach Electronics electrical fault detection electrical fault classification three-phase induction motor wavelet packet transform wavelet power energy and moving window technique |
| title | Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach |
| title_full | Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach |
| title_fullStr | Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach |
| title_full_unstemmed | Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach |
| title_short | Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach |
| title_sort | detection and diagnosis of stator and rotor electrical faults for three phase induction motor via wavelet energy approach |
| topic | electrical fault detection electrical fault classification three-phase induction motor wavelet packet transform wavelet power energy and moving window technique |
| url | https://www.mdpi.com/2079-9292/11/8/1253 |
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