DETECTION OF STATIC AIR-GAP ECCENTRICITY IN THREE PHASE INDUCTION MOTOR BY USING ARTIFICIAL NEURAL NETWORK (ANN)
This paper presents the effect of the static air-gap eccentricity on the performance of a three phase induction motor .The Artificial Neural Network (ANN) approach has been used to detect this fault .This technique depends upon the amplitude of the positive and negative harmonics of the frequency....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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University of Baghdad
2009-12-01
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| Series: | Journal of Engineering |
| Subjects: | |
| Online Access: | https://joe.uobaghdad.edu.iq/index.php/main/article/view/3156 |
| _version_ | 1797295708557017088 |
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| author | Qais S. Al-Sabbagh Hayder E. Alwan |
| author_facet | Qais S. Al-Sabbagh Hayder E. Alwan |
| author_sort | Qais S. Al-Sabbagh |
| collection | DOAJ |
| description |
This paper presents the effect of the static air-gap eccentricity on the performance of a three phase induction motor .The Artificial Neural Network (ANN) approach has been used to detect this fault .This technique depends upon the amplitude of the positive and negative harmonics of the frequency. Two motors of (2.2 kW) have been used to achieve the actual fault and desirable data at no-load, half-load and full-load conditions. Motor Current Signature analysis (MCSA) based on stator current has been used to detect eccentricity fault. Feed forward neural network and error back propagation training algorithms are used to perform the motor fault detection. The inputs of artificial neural network are the amplitudes of the positive and negative harmonics and the speed, and the output is the type of fault. The training of neural network is achieved by data through the experiments test on healthy and faulty motor and the diagnostic system can discriminate between “healthy” and “faulty” machine.
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| first_indexed | 2024-03-07T21:51:53Z |
| format | Article |
| id | doaj.art-ecd3d6b80075449b903facd937fa41bb |
| institution | Directory Open Access Journal |
| issn | 1726-4073 2520-3339 |
| language | English |
| last_indexed | 2024-03-07T21:51:53Z |
| publishDate | 2009-12-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj.art-ecd3d6b80075449b903facd937fa41bb2024-02-25T09:49:34ZengUniversity of BaghdadJournal of Engineering1726-40732520-33392009-12-01150410.31026/j.eng.2009.04.06DETECTION OF STATIC AIR-GAP ECCENTRICITY IN THREE PHASE INDUCTION MOTOR BY USING ARTIFICIAL NEURAL NETWORK (ANN)Qais S. Al-SabbaghHayder E. Alwan This paper presents the effect of the static air-gap eccentricity on the performance of a three phase induction motor .The Artificial Neural Network (ANN) approach has been used to detect this fault .This technique depends upon the amplitude of the positive and negative harmonics of the frequency. Two motors of (2.2 kW) have been used to achieve the actual fault and desirable data at no-load, half-load and full-load conditions. Motor Current Signature analysis (MCSA) based on stator current has been used to detect eccentricity fault. Feed forward neural network and error back propagation training algorithms are used to perform the motor fault detection. The inputs of artificial neural network are the amplitudes of the positive and negative harmonics and the speed, and the output is the type of fault. The training of neural network is achieved by data through the experiments test on healthy and faulty motor and the diagnostic system can discriminate between “healthy” and “faulty” machine. https://joe.uobaghdad.edu.iq/index.php/main/article/view/3156Static Eccentricity, Three Phase Induction Motor, Artificial Neural Network |
| spellingShingle | Qais S. Al-Sabbagh Hayder E. Alwan DETECTION OF STATIC AIR-GAP ECCENTRICITY IN THREE PHASE INDUCTION MOTOR BY USING ARTIFICIAL NEURAL NETWORK (ANN) Journal of Engineering Static Eccentricity, Three Phase Induction Motor, Artificial Neural Network |
| title | DETECTION OF STATIC AIR-GAP ECCENTRICITY IN THREE PHASE INDUCTION MOTOR BY USING ARTIFICIAL NEURAL NETWORK (ANN) |
| title_full | DETECTION OF STATIC AIR-GAP ECCENTRICITY IN THREE PHASE INDUCTION MOTOR BY USING ARTIFICIAL NEURAL NETWORK (ANN) |
| title_fullStr | DETECTION OF STATIC AIR-GAP ECCENTRICITY IN THREE PHASE INDUCTION MOTOR BY USING ARTIFICIAL NEURAL NETWORK (ANN) |
| title_full_unstemmed | DETECTION OF STATIC AIR-GAP ECCENTRICITY IN THREE PHASE INDUCTION MOTOR BY USING ARTIFICIAL NEURAL NETWORK (ANN) |
| title_short | DETECTION OF STATIC AIR-GAP ECCENTRICITY IN THREE PHASE INDUCTION MOTOR BY USING ARTIFICIAL NEURAL NETWORK (ANN) |
| title_sort | detection of static air gap eccentricity in three phase induction motor by using artificial neural network ann |
| topic | Static Eccentricity, Three Phase Induction Motor, Artificial Neural Network |
| url | https://joe.uobaghdad.edu.iq/index.php/main/article/view/3156 |
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