Fault State Recognition of Rolling Bearing Based Fully Convolutional Network
To solve the problem of determining the fault damage of rolling bearings, a fault diagnosis method for intelligent classification of vibration signals with different fault locations and different damage degrees is proposed. First, the research object is the laboratory dataset. By transforming into s...
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| Veröffentlicht in: | Computing in science & engineering 2019-09, Vol.21 (5), p.55-63 |
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| container_title | Computing in science & engineering |
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| creator | Zhang, Wendong Zhang, Fan Chen, Wei Jiang, Yongquan Song, Dongli |
| description | To solve the problem of determining the fault damage of rolling bearings, a fault diagnosis method for intelligent classification of vibration signals with different fault locations and different damage degrees is proposed. First, the research object is the laboratory dataset. By transforming into spectrograms, this can preserve the original information of the time-domain signal to a greater extent. Then, we use a deep, fully convolutional neural network to train the dataset. It has a rapid convergence and the accuracy is up to 100%. Second, in order to verify the correctness of the model, we take the service data on the real line as the research object, and the accuracy rate is as high as 99.22%. Compared with some other machine learning algorithms, our method boasts better generalization capability and accuracy and could be applied to practical engineering. |
| doi_str_mv | 10.1109/MCSE.2018.110113254 |
| format | Article |
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First, the research object is the laboratory dataset. By transforming into spectrograms, this can preserve the original information of the time-domain signal to a greater extent. Then, we use a deep, fully convolutional neural network to train the dataset. It has a rapid convergence and the accuracy is up to 100%. Second, in order to verify the correctness of the model, we take the service data on the real line as the research object, and the accuracy rate is as high as 99.22%. Compared with some other machine learning algorithms, our method boasts better generalization capability and accuracy and could be applied to practical engineering.</description><identifier>ISSN: 1521-9615</identifier><identifier>EISSN: 1558-366X</identifier><identifier>DOI: 10.1109/MCSE.2018.110113254</identifier><identifier>CODEN: CSENFA</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Accuracy ; Algorithms ; Artificial neural networks ; Convolutional neural networks ; Datasets ; deep fault diagnosis system ; deep learning ; Fault diagnosis ; fault state recognition ; Feature extraction ; Machine learning ; Object recognition ; Roller bearings ; rolling bearing ; Rolling bearings ; Signal classification ; Spectrograms ; Time-frequency analysis</subject><ispartof>Computing in science & engineering, 2019-09, Vol.21 (5), p.55-63</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c297t-6913cfbf42eda20a34a06bd8f04b8e3bf51af61afdc4a7d8c9d1967a3f6fe5083</citedby><cites>FETCH-LOGICAL-c297t-6913cfbf42eda20a34a06bd8f04b8e3bf51af61afdc4a7d8c9d1967a3f6fe5083</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8254321$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8254321$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhang, Wendong</creatorcontrib><creatorcontrib>Zhang, Fan</creatorcontrib><creatorcontrib>Chen, Wei</creatorcontrib><creatorcontrib>Jiang, Yongquan</creatorcontrib><creatorcontrib>Song, Dongli</creatorcontrib><title>Fault State Recognition of Rolling Bearing Based Fully Convolutional Network</title><title>Computing in science & engineering</title><addtitle>CISE-M</addtitle><description>To solve the problem of determining the fault damage of rolling bearings, a fault diagnosis method for intelligent classification of vibration signals with different fault locations and different damage degrees is proposed. 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Compared with some other machine learning algorithms, our method boasts better generalization capability and accuracy and could be applied to practical engineering.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Convolutional neural networks</subject><subject>Datasets</subject><subject>deep fault diagnosis system</subject><subject>deep learning</subject><subject>Fault diagnosis</subject><subject>fault state recognition</subject><subject>Feature extraction</subject><subject>Machine learning</subject><subject>Object recognition</subject><subject>Roller bearings</subject><subject>rolling bearing</subject><subject>Rolling bearings</subject><subject>Signal classification</subject><subject>Spectrograms</subject><subject>Time-frequency analysis</subject><issn>1521-9615</issn><issn>1558-366X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kN9LwzAQx4MoOKd_wV4CPnfmR5Omj1o2FarCpuBbSNtkdMZmJqmy_97WyR6Ou-O-3-PuA8AMoznGKL95KtaLOUFYjC3GlLD0BEwwYyKhnL-fjjXBSc4xOwcXIWwRQqnI2QSUS9XbCNdRRQ1Xunabro2t66AzcOWsbbsNvNPK_2UVdAOXvbV7WLju29l-lCoLn3X8cf7jEpwZZYO--s9T8LZcvBYPSfly_1jclklN8iwmPMe0NpVJiW4UQYqmCvGqEQalldC0Mgwrw4do6lRljajzBuc8U9RwoxkSdAquD3t33n31OkS5db0fDgmSkGz4mqCMDSp6UNXeheC1kTvffiq_lxjJEZscsckRmzxiG1yzg6vVWh8dYhwRTH8BKbVpuQ</recordid><startdate>201909</startdate><enddate>201909</enddate><creator>Zhang, Wendong</creator><creator>Zhang, Fan</creator><creator>Chen, Wei</creator><creator>Jiang, Yongquan</creator><creator>Song, Dongli</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Electronic Library (IEL) |
| subjects | Accuracy Algorithms Artificial neural networks Convolutional neural networks Datasets deep fault diagnosis system deep learning Fault diagnosis fault state recognition Feature extraction Machine learning Object recognition Roller bearings rolling bearing Rolling bearings Signal classification Spectrograms Time-frequency analysis |
| title | Fault State Recognition of Rolling Bearing Based Fully Convolutional Network |
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