Fault detection in flotation processes based on deep learning and support vector machine
Effective fault detection techniques can help flotation plant reduce reagents consumption, increase mineral recovery, and reduce labor intensity. Traditional, online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation, like col...
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Veröffentlicht in: | Journal of Central South University 2019-09, Vol.26 (9), p.2504-2515 |
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description | Effective fault detection techniques can help flotation plant reduce reagents consumption, increase mineral recovery, and reduce labor intensity. Traditional, online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation, like color, shape, size and texture, always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case. In this work, a new integrated method based on convolution neural network (CNN) combined with transfer learning approach and support vector machine (SVM) is proposed to automatically recognize the flotation condition. To be more specific, CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection. As compared with the existed recognition methods, it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy. Hence, a CNN-SVM based, real-time flotation monitoring system is proposed for application in an antimony flotation plant in China. |
doi_str_mv | 10.1007/s11771-019-4190-8 |
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Traditional, online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation, like color, shape, size and texture, always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case. In this work, a new integrated method based on convolution neural network (CNN) combined with transfer learning approach and support vector machine (SVM) is proposed to automatically recognize the flotation condition. To be more specific, CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection. As compared with the existed recognition methods, it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy. Hence, a CNN-SVM based, real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.</description><identifier>ISSN: 2095-2899</identifier><identifier>EISSN: 2227-5223</identifier><identifier>DOI: 10.1007/s11771-019-4190-8</identifier><language>eng</language><publisher>Changsha: Central South University</publisher><subject>Algorithms ; Antimony ; Artificial neural networks ; Chemical reduction ; Convolution ; Deep learning ; Engineering ; Fault detection ; Feature extraction ; Flotation ; Image detection ; Image segmentation ; Metallic Materials ; Reagents ; Support vector machines</subject><ispartof>Journal of Central South University, 2019-09, Vol.26 (9), p.2504-2515</ispartof><rights>Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019</rights><rights>Copyright Springer Nature B.V. 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-92f641f64236146b742b1084c0dcf17d208d989a13d3ae4b9f52437058bb29ea3</citedby><cites>FETCH-LOGICAL-c316t-92f641f64236146b742b1084c0dcf17d208d989a13d3ae4b9f52437058bb29ea3</cites><orcidid>0000-0003-0312-436X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11771-019-4190-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11771-019-4190-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Li, Zhong-mei</creatorcontrib><creatorcontrib>Gui, Wei-hua</creatorcontrib><creatorcontrib>Zhu, Jian-yong</creatorcontrib><title>Fault detection in flotation processes based on deep learning and support vector machine</title><title>Journal of Central South University</title><addtitle>J. Cent. South Univ</addtitle><description>Effective fault detection techniques can help flotation plant reduce reagents consumption, increase mineral recovery, and reduce labor intensity. Traditional, online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation, like color, shape, size and texture, always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case. In this work, a new integrated method based on convolution neural network (CNN) combined with transfer learning approach and support vector machine (SVM) is proposed to automatically recognize the flotation condition. To be more specific, CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection. As compared with the existed recognition methods, it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy. Hence, a CNN-SVM based, real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.</description><subject>Algorithms</subject><subject>Antimony</subject><subject>Artificial neural networks</subject><subject>Chemical reduction</subject><subject>Convolution</subject><subject>Deep learning</subject><subject>Engineering</subject><subject>Fault detection</subject><subject>Feature extraction</subject><subject>Flotation</subject><subject>Image detection</subject><subject>Image segmentation</subject><subject>Metallic Materials</subject><subject>Reagents</subject><subject>Support vector machines</subject><issn>2095-2899</issn><issn>2227-5223</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kEFLxDAQhYMouKz7A7wFPEczk7ZpjrK4Kix4UfAW0iZdK7tpTVLBf2_WCp48DDMZ3nsTPkIugV8D5_ImAkgJjINiBSjO6hOyQETJSkRxmmeuSoa1UudkFWPfcAFYiUpVC_K6MdM-UeuSa1M_eNp72u2HZH4eYxhaF6OLtDHRWZpX1rmR7p0Jvvc7arylcRrHIST6mROGQA-mfeu9uyBnndlHt_rtS_KyuXteP7Dt0_3j-nbLWgFVYgq7qoBcKCooqkYW2ACvi5bbtgNpkddW1cqAsMK4olFdiYWQvKybBpUzYkmu5tz814_JxaTfhyn4fFKj4GWJpUTIKphVbRhiDK7TY-gPJnxp4PrIUM8MdWaojwx1nT04e2LW-p0Lf8n_m74BKjFztg</recordid><startdate>20190901</startdate><enddate>20190901</enddate><creator>Li, Zhong-mei</creator><creator>Gui, Wei-hua</creator><creator>Zhu, Jian-yong</creator><general>Central South University</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-0312-436X</orcidid></search><sort><creationdate>20190901</creationdate><title>Fault detection in flotation processes based on deep learning and support vector machine</title><author>Li, Zhong-mei ; Gui, Wei-hua ; Zhu, Jian-yong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-92f641f64236146b742b1084c0dcf17d208d989a13d3ae4b9f52437058bb29ea3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Antimony</topic><topic>Artificial neural networks</topic><topic>Chemical reduction</topic><topic>Convolution</topic><topic>Deep learning</topic><topic>Engineering</topic><topic>Fault detection</topic><topic>Feature extraction</topic><topic>Flotation</topic><topic>Image detection</topic><topic>Image segmentation</topic><topic>Metallic Materials</topic><topic>Reagents</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Zhong-mei</creatorcontrib><creatorcontrib>Gui, Wei-hua</creatorcontrib><creatorcontrib>Zhu, Jian-yong</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of Central South University</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Zhong-mei</au><au>Gui, Wei-hua</au><au>Zhu, Jian-yong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fault detection in flotation processes based on deep learning and support vector machine</atitle><jtitle>Journal of Central South University</jtitle><stitle>J. Cent. South Univ</stitle><date>2019-09-01</date><risdate>2019</risdate><volume>26</volume><issue>9</issue><spage>2504</spage><epage>2515</epage><pages>2504-2515</pages><issn>2095-2899</issn><eissn>2227-5223</eissn><abstract>Effective fault detection techniques can help flotation plant reduce reagents consumption, increase mineral recovery, and reduce labor intensity. Traditional, online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation, like color, shape, size and texture, always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case. In this work, a new integrated method based on convolution neural network (CNN) combined with transfer learning approach and support vector machine (SVM) is proposed to automatically recognize the flotation condition. To be more specific, CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection. As compared with the existed recognition methods, it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy. Hence, a CNN-SVM based, real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.</abstract><cop>Changsha</cop><pub>Central South University</pub><doi>10.1007/s11771-019-4190-8</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-0312-436X</orcidid></addata></record> |
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subjects | Algorithms Antimony Artificial neural networks Chemical reduction Convolution Deep learning Engineering Fault detection Feature extraction Flotation Image detection Image segmentation Metallic Materials Reagents Support vector machines |
title | Fault detection in flotation processes based on deep learning and support vector machine |
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