Statistical and Neural-Network Approaches for the Classification of Induction Machine Faults Using the Ambiguity Plane Representation
A novel hybrid feature-reduction methodology is proposed as a contribution to the induction motor fault classification, to improve the classification rate of the current waveform events related to varieties of induction machine faults. This methodology relies on the combination of a feature-extracti...
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| Veröffentlicht in: | IEEE transactions on industrial electronics (1982) 2013-09, Vol.60 (9), p.4034-4042 |
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| container_issue | 9 |
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| container_title | IEEE transactions on industrial electronics (1982) |
| container_volume | 60 |
| creator | Boukra, T. Lebaroud, A. Clerc, G. |
| description | A novel hybrid feature-reduction methodology is proposed as a contribution to the induction motor fault classification, to improve the classification rate of the current waveform events related to varieties of induction machine faults. This methodology relies on the combination of a feature-extraction technique based on the smoothed ambiguity plane designed for maximizing the separability between classes using Fisher's discriminant ratio, with the feature-selection technique, based on the proposed error-probability model to select an optimal number of the extracted features. This model depends on two parameters, namely, the smoothing kernel used to derive the features and the distance measurement. The proposed methodology is validated experimentally on a 5.5-kW induction motor test bench, and their performances are compared with the classification algorithm based on neural networks with sigmoid and wavelets in hidden neurons, known as a flexible tool for learning and recognizing system faults. The results obtained show an accurate classification independent from the load level. |
| doi_str_mv | 10.1109/TIE.2012.2216242 |
| format | Article |
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This methodology relies on the combination of a feature-extraction technique based on the smoothed ambiguity plane designed for maximizing the separability between classes using Fisher's discriminant ratio, with the feature-selection technique, based on the proposed error-probability model to select an optimal number of the extracted features. This model depends on two parameters, namely, the smoothing kernel used to derive the features and the distance measurement. The proposed methodology is validated experimentally on a 5.5-kW induction motor test bench, and their performances are compared with the classification algorithm based on neural networks with sigmoid and wavelets in hidden neurons, known as a flexible tool for learning and recognizing system faults. 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(IEEE) Sep 2013</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c358t-ed27fe46a0f5faf3e7962844a4796c1c351f815f19382b5fc5951dba5663d6493</citedby><cites>FETCH-LOGICAL-c358t-ed27fe46a0f5faf3e7962844a4796c1c351f815f19382b5fc5951dba5663d6493</cites><orcidid>0000-0001-7202-056X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6290359$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,776,780,792,881,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6290359$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://hal.science/hal-00904827$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Boukra, T.</creatorcontrib><creatorcontrib>Lebaroud, A.</creatorcontrib><creatorcontrib>Clerc, G.</creatorcontrib><title>Statistical and Neural-Network Approaches for the Classification of Induction Machine Faults Using the Ambiguity Plane Representation</title><title>IEEE transactions on industrial electronics (1982)</title><addtitle>TIE</addtitle><description>A novel hybrid feature-reduction methodology is proposed as a contribution to the induction motor fault classification, to improve the classification rate of the current waveform events related to varieties of induction machine faults. This methodology relies on the combination of a feature-extraction technique based on the smoothed ambiguity plane designed for maximizing the separability between classes using Fisher's discriminant ratio, with the feature-selection technique, based on the proposed error-probability model to select an optimal number of the extracted features. This model depends on two parameters, namely, the smoothing kernel used to derive the features and the distance measurement. The proposed methodology is validated experimentally on a 5.5-kW induction motor test bench, and their performances are compared with the classification algorithm based on neural networks with sigmoid and wavelets in hidden neurons, known as a flexible tool for learning and recognizing system faults. The results obtained show an accurate classification independent from the load level.</description><subject>Algorithms</subject><subject>Ambiguity</subject><subject>Ambiguity plane</subject><subject>Artificial neural networks</subject><subject>artificial neural networks (ANNs)</subject><subject>Classification</subject><subject>discrete wavelet transforms</subject><subject>distance measurement</subject><subject>Electric power</subject><subject>Engineering Sciences</subject><subject>Error probability</subject><subject>fault diagnosis</subject><subject>Faults</subject><subject>Feature extraction</subject><subject>frequency-domain analysis</subject><subject>Induction motors</subject><subject>Kernel</subject><subject>Methodology</subject><subject>Motors</subject><subject>Neural networks</subject><subject>Planes</subject><subject>Studies</subject><subject>time-domain analysis</subject><subject>time-frequency-domain analysis</subject><subject>Training</subject><issn>0278-0046</issn><issn>1557-9948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkU1vEzEQhi0EEqFwR-JiiQscNvh77WMUtTRSKAjas-XsjhuXzTrYXlB_QP93naTqgZPH1vOOxvMg9J6SOaXEfLlenc8ZoWzOGFVMsBdoRqVsG2OEfolmhLW6IUSo1-hNzneEUCGpnKGHX8WVkEvo3IDd2OMrmJIbmiso_2L6jRf7fYqu20LGPiZctoCXg8s5-JooIY44erwa-6k7Xr5VNIyAL9w0lIxvchhvj6HFbhNup1Du8Y_BVeAn7BNkGMuxyVv0yrshw7un8wzdXJxfLy-b9fevq-Vi3XRc6tJAz1oPQjnipXeeQ2sU00I4UYuOVoh6TaWnhmu2kb6TRtJ-46RSvFfC8DP0-dR36wa7T2Hn0r2NLtjLxdoe3ggxRGjW_qWV_XRi6wL-TJCL3YXcwXAYP07ZUt4yRYzmbUU__ofexSmN9SeVEkYrUVVUipyoLsWcE_jnCSixB4e2OrQHh_bJYY18OEUCADzjihnCpeGPHeiX3A</recordid><startdate>20130901</startdate><enddate>20130901</enddate><creator>Boukra, T.</creator><creator>Lebaroud, A.</creator><creator>Clerc, G.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| ispartof | IEEE transactions on industrial electronics (1982), 2013-09, Vol.60 (9), p.4034-4042 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Ambiguity Ambiguity plane Artificial neural networks artificial neural networks (ANNs) Classification discrete wavelet transforms distance measurement Electric power Engineering Sciences Error probability fault diagnosis Faults Feature extraction frequency-domain analysis Induction motors Kernel Methodology Motors Neural networks Planes Studies time-domain analysis time-frequency-domain analysis Training |
| title | Statistical and Neural-Network Approaches for the Classification of Induction Machine Faults Using the Ambiguity Plane Representation |
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