Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network
Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lea...
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| Veröffentlicht in: | Computational Intelligence and Neuroscience 2016-01, Vol.2016 (2016), p.419-431 |
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| creator | Shmshir, Muhammad Uyaroğlu, Yılmaz Bayır, Raif |
| description | Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured. |
| doi_str_mv | 10.1155/2016/7129376 |
| format | Article |
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Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.</description><identifier>ISSN: 1687-5265</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2016/7129376</identifier><identifier>PMID: 26819590</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Limiteds</publisher><subject>Algorithms ; Back propagation ; CAD ; Computer aided design ; Computer Simulation ; Datasets ; Electric motors ; Electric Power Supplies ; Electronics ; Evaluation ; Fault diagnosis ; Fault location (Engineering) ; Feedforward ; Fuzzy logic ; Humans ; Machine Learning ; Mathematical models ; Matlab ; Methods ; Models, Theoretical ; Motors ; Neural networks ; Neural Networks (Computer) ; Parameter estimation ; Real time ; Real-time control ; Real-time systems ; Sensors ; Solar power generation</subject><ispartof>Computational Intelligence and Neuroscience, 2016-01, Vol.2016 (2016), p.419-431</ispartof><rights>Copyright © 2016 Mehmet Şimşir et al.</rights><rights>COPYRIGHT 2015 John Wiley & Sons, Inc.</rights><rights>COPYRIGHT 2016 John Wiley & Sons, Inc.</rights><rights>Copyright © 2016 Mehmet Simsir et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright © 2016 Mehmet Şimşir et al. 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a722t-65dbaa37a4f17dca4e003fd653c91c1dbddddcc03a197234ae1a07bb3bb546ce3</citedby><cites>FETCH-LOGICAL-a722t-65dbaa37a4f17dca4e003fd653c91c1dbddddcc03a197234ae1a07bb3bb546ce3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4706884/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4706884/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26819590$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Schmuker, Michael</contributor><creatorcontrib>Shmshir, Muhammad</creatorcontrib><creatorcontrib>Uyaroğlu, Yılmaz</creatorcontrib><creatorcontrib>Bayır, Raif</creatorcontrib><title>Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network</title><title>Computational Intelligence and Neuroscience</title><addtitle>Comput Intell Neurosci</addtitle><description>Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.</description><subject>Algorithms</subject><subject>Back propagation</subject><subject>CAD</subject><subject>Computer aided design</subject><subject>Computer Simulation</subject><subject>Datasets</subject><subject>Electric motors</subject><subject>Electric Power Supplies</subject><subject>Electronics</subject><subject>Evaluation</subject><subject>Fault diagnosis</subject><subject>Fault location (Engineering)</subject><subject>Feedforward</subject><subject>Fuzzy logic</subject><subject>Humans</subject><subject>Machine Learning</subject><subject>Mathematical models</subject><subject>Matlab</subject><subject>Methods</subject><subject>Models, Theoretical</subject><subject>Motors</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Parameter 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Neurosci</addtitle><date>2016-01-01</date><risdate>2016</risdate><volume>2016</volume><issue>2016</issue><spage>419</spage><epage>431</epage><pages>419-431</pages><issn>1687-5265</issn><eissn>1687-5273</eissn><abstract>Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Limiteds</pub><pmid>26819590</pmid><doi>10.1155/2016/7129376</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Back propagation CAD Computer aided design Computer Simulation Datasets Electric motors Electric Power Supplies Electronics Evaluation Fault diagnosis Fault location (Engineering) Feedforward Fuzzy logic Humans Machine Learning Mathematical models Matlab Methods Models, Theoretical Motors Neural networks Neural Networks (Computer) Parameter estimation Real time Real-time control Real-time systems Sensors Solar power generation |
| title | Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network |
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