Induction machine diagnostic using adaptive neuro fuzzy inferencing system
Electrical machines are subjected to wear and tear after being used for sometime and proper maintenance is required to prevent breakdown. One of the main maintenance efforts is to detect fault occurring in the electrical machines. Some of these faults are slowly developing faults and early detection...
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| Format: | Book Section |
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Springer Berlin / Heidelberg
2004
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| Online Access: | http://eprints.utm.my/7156/ https://link.springer.com/chapter/10.1007/978-3-540-30134-9_51 http://dx.doi.org/10.1007/b100916 |
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| _version_ | 1845471755534073856 |
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| author | Shukri, Mohamad Khalid, Marzuki Yusuf, Rubiyah Shafawi, Mohd. |
| author_facet | Shukri, Mohamad Khalid, Marzuki Yusuf, Rubiyah Shafawi, Mohd. |
| author_sort | Shukri, Mohamad |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Electrical machines are subjected to wear and tear after being used for sometime and proper maintenance is required to prevent breakdown. One of the main maintenance efforts is to detect fault occurring in the electrical machines. Some of these faults are slowly developing faults and early detection of these faults is crucial to prevent machine breakdown. In this paper, we investigate the effectiveness of a fault detection and diagnosis system using adaptive neuro fuzzy inferencing system (ANFIS) on a simulated three-phase induction motor. Several parameters of the induction motor are adjusted to represent faulty conditions. The experimental results obtained show that the algorithm has good fault detection and diagnosis ability.
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| format | Book Section |
| id | my.utm.eprints-7156 |
| institution | Universiti Teknologi Malaysia |
| publishDate | 2004 |
| publisher | Springer Berlin / Heidelberg |
| record_format | eprints |
| spelling | my.utm.eprints-71562017-08-13T07:57:06Z http://eprints.utm.my/7156/ Induction machine diagnostic using adaptive neuro fuzzy inferencing system Shukri, Mohamad Khalid, Marzuki Yusuf, Rubiyah Shafawi, Mohd. QA75 Electronic computers. Computer science Electrical machines are subjected to wear and tear after being used for sometime and proper maintenance is required to prevent breakdown. One of the main maintenance efforts is to detect fault occurring in the electrical machines. Some of these faults are slowly developing faults and early detection of these faults is crucial to prevent machine breakdown. In this paper, we investigate the effectiveness of a fault detection and diagnosis system using adaptive neuro fuzzy inferencing system (ANFIS) on a simulated three-phase induction motor. Several parameters of the induction motor are adjusted to represent faulty conditions. The experimental results obtained show that the algorithm has good fault detection and diagnosis ability. Springer Berlin / Heidelberg 2004 Book Section PeerReviewed Shukri, Mohamad and Khalid, Marzuki and Yusuf, Rubiyah and Shafawi, Mohd. (2004) Induction machine diagnostic using adaptive neuro fuzzy inferencing system. In: Knowledge-Based Intelligent Information and Engineering Systems. Lecture Notes in Computer Science , 3215/2004 . Springer Berlin / Heidelberg, Germany, pp. 380-387. ISBN 978-3-540-23205-6 https://link.springer.com/chapter/10.1007/978-3-540-30134-9_51 http://dx.doi.org/10.1007/b100916 |
| spellingShingle | QA75 Electronic computers. Computer science Shukri, Mohamad Khalid, Marzuki Yusuf, Rubiyah Shafawi, Mohd. Induction machine diagnostic using adaptive neuro fuzzy inferencing system |
| title | Induction machine diagnostic using adaptive neuro fuzzy inferencing system |
| title_full | Induction machine diagnostic using adaptive neuro fuzzy inferencing system |
| title_fullStr | Induction machine diagnostic using adaptive neuro fuzzy inferencing system |
| title_full_unstemmed | Induction machine diagnostic using adaptive neuro fuzzy inferencing system |
| title_short | Induction machine diagnostic using adaptive neuro fuzzy inferencing system |
| title_sort | induction machine diagnostic using adaptive neuro fuzzy inferencing system |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/7156/ https://link.springer.com/chapter/10.1007/978-3-540-30134-9_51 http://dx.doi.org/10.1007/b100916 |
| url_provider | http://eprints.utm.my/ |