A neural network approach to real-time condition monitoring of induction motors
A neural network-based incipient fault detector for small and medium-size induction motors is developed. The detector avoids the problems associated with traditional incipient fault detection schemes by employing more readily available information such as rotor speed and stator current. The neural n...
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| Published in | IEEE transactions on industrial electronics (1982) Vol. 38; no. 6; pp. 448 - 453 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.12.1991
Institute of Electrical and Electronics Engineers |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0278-0046 |
| DOI | 10.1109/41.107100 |
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| Abstract | A neural network-based incipient fault detector for small and medium-size induction motors is developed. The detector avoids the problems associated with traditional incipient fault detection schemes by employing more readily available information such as rotor speed and stator current. The neural network design is evaluated in real time in the laboratory on a 3/4 hp permanent magnet induction motor. The results of this evaluation indicate that the neural-network-based incipient fault detector provides a satisfactory level of accuracy, greater than 95%, which is suitable for real-world applications.< > |
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| AbstractList | A neural network-based incipient fault detector for small and medium-size induction motors is developed. The detector avoids the problems associated with traditional incipient fault detection schemes by employing more readily available information such as rotor speed and stator current. The neural network design is evaluated in real time in the laboratory on a 3/4 hp permanent magnet induction motor. The results of this evaluation indicate that the neural-network-based incipient fault detector provides a satisfactory level of accuracy, greater than 95%, which is suitable for real-world applications A neural network-based incipient fault detector for small and medium-size induction motors is developed. The detector avoids the problems associated with traditional incipient fault detection schemes by employing more readily available information such as rotor speed and stator current. The neural network design is evaluated in real time in the laboratory on a 3/4 hp permanent magnet induction motor. The results of this evaluation indicate that the neural-network-based incipient fault detector provides a satisfactory level of accuracy, greater than 95%, which is suitable for real-world applications.< > |
| Author | Chow, M.-y. Yee, S.O. Mangum, P.M. |
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| Cites_doi | 10.1109/MASSP.1987.1165576 10.2172/7101510 10.1109/TPAS.1974.293858 10.1142/S012906579100008X 10.7551/mitpress/5236.001.0001 |
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| References | ref8 chow (ref4) 1990 rummelhart (ref7) 1986 werbos (ref9) 1974 smeaton (ref6) 1987 cambrias (ref2) 1988 ref3 ref5 tavner (ref1) 1989 |
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| SubjectTerms | A.c. Machines Applied sciences Artificial neural networks Condition monitoring Electrical engineering. Electrical power engineering Electrical fault detection Electrical machines Exact sciences and technology Fault detection Induction motors Laboratories Neural networks Rotating machines Rotors Stator windings |
| Title | A neural network approach to real-time condition monitoring of induction motors |
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