A System Fault Diagnosis Method with a Reclustering Algorithm
The log analysis-based system fault diagnosis method can help engineers analyze the fault events generated by the system. The K-means algorithm can perform log analysis well and does not require a lot of prior knowledge, but the K-means-based system fault diagnosis method needs to be improved in bot...
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| Veröffentlicht in: | Scientific programming 2021-03, Vol.2021, p.1-8 |
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| container_title | Scientific programming |
| container_volume | 2021 |
| creator | Yang, Zhe Ying, Shi Wang, Bingming Li, Yiyao Dong, Bo Geng, Jiangyi Zhang, Ting |
| description | The log analysis-based system fault diagnosis method can help engineers analyze the fault events generated by the system. The K-means algorithm can perform log analysis well and does not require a lot of prior knowledge, but the K-means-based system fault diagnosis method needs to be improved in both efficiency and accuracy. To solve this problem, we propose a system fault diagnosis method based on a reclustering algorithm. First, we propose a log vectorization method based on the PV-DM language model to obtain low-dimensional log vectors which can provide effective data support for the subsequent fault diagnosis; then, we improve the K-means algorithm and make the effect of K-means algorithm based log clustering; finally, we propose a reclustering method based on keywords’ extraction to improve the accuracy of fault diagnosis. We use system log data generated by two supercomputers to verify our method. The experimental results show that compared with the traditional K-means method, our method can improve the accuracy of fault diagnosis while ensuring the efficiency of fault diagnosis. |
| doi_str_mv | 10.1155/2021/6617882 |
| format | Article |
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The K-means algorithm can perform log analysis well and does not require a lot of prior knowledge, but the K-means-based system fault diagnosis method needs to be improved in both efficiency and accuracy. To solve this problem, we propose a system fault diagnosis method based on a reclustering algorithm. First, we propose a log vectorization method based on the PV-DM language model to obtain low-dimensional log vectors which can provide effective data support for the subsequent fault diagnosis; then, we improve the K-means algorithm and make the effect of K-means algorithm based log clustering; finally, we propose a reclustering method based on keywords’ extraction to improve the accuracy of fault diagnosis. We use system log data generated by two supercomputers to verify our method. The experimental results show that compared with the traditional K-means method, our method can improve the accuracy of fault diagnosis while ensuring the efficiency of fault diagnosis.</description><identifier>ISSN: 1058-9244</identifier><identifier>EISSN: 1875-919X</identifier><identifier>DOI: 10.1155/2021/6617882</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Accuracy ; Algorithms ; Clustering ; Failure ; Fault diagnosis ; Machine learning ; Neural networks ; Semantics ; Software ; Supercomputers</subject><ispartof>Scientific programming, 2021-03, Vol.2021, p.1-8</ispartof><rights>Copyright © 2021 Zhe Yang et al.</rights><rights>Copyright © 2021 Zhe Yang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-bfb4d11eae3e7d62417a80ca8dc5232e4696484652e863f3c3c680ce57b96a153</citedby><orcidid>0000-0002-0471-0021 ; 0000-0002-8723-0970</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,778,782,27907,27908</link.rule.ids></links><search><contributor>Wang, Pengwei</contributor><contributor>Pengwei Wang</contributor><creatorcontrib>Yang, Zhe</creatorcontrib><creatorcontrib>Ying, Shi</creatorcontrib><creatorcontrib>Wang, Bingming</creatorcontrib><creatorcontrib>Li, Yiyao</creatorcontrib><creatorcontrib>Dong, Bo</creatorcontrib><creatorcontrib>Geng, Jiangyi</creatorcontrib><creatorcontrib>Zhang, Ting</creatorcontrib><title>A System Fault Diagnosis Method with a Reclustering Algorithm</title><title>Scientific programming</title><description>The log analysis-based system fault diagnosis method can help engineers analyze the fault events generated by the system. The K-means algorithm can perform log analysis well and does not require a lot of prior knowledge, but the K-means-based system fault diagnosis method needs to be improved in both efficiency and accuracy. To solve this problem, we propose a system fault diagnosis method based on a reclustering algorithm. First, we propose a log vectorization method based on the PV-DM language model to obtain low-dimensional log vectors which can provide effective data support for the subsequent fault diagnosis; then, we improve the K-means algorithm and make the effect of K-means algorithm based log clustering; finally, we propose a reclustering method based on keywords’ extraction to improve the accuracy of fault diagnosis. We use system log data generated by two supercomputers to verify our method. The experimental results show that compared with the traditional K-means method, our method can improve the accuracy of fault diagnosis while ensuring the efficiency of fault diagnosis.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Clustering</subject><subject>Failure</subject><subject>Fault diagnosis</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Semantics</subject><subject>Software</subject><subject>Supercomputers</subject><issn>1058-9244</issn><issn>1875-919X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><recordid>eNp90E1LAzEQBuAgCtbqzR8Q8KhrM_na7MFDqVaFiuAHeAtpNtumbHdrskvpvzelPXuaYeZhBl6EroHcAwgxooTCSErIlaInaAAqF1kBxc9p6olQWUE5P0cXMa4IAQWEDNDDGH_uYufWeGr6usOP3iyaNvqI31y3bEu89d0SG_zhbN0nF3yzwON60YY0X1-is8rU0V0d6xB9T5--Ji_Z7P35dTKeZZaxvMvm1ZyXAM445vJSUg65UcQaVVpBGXVcFpIrLgV1SrKKWWZl2juRzwtpQLAhujnc3YT2t3ex06u2D016qakgjAkKBUvq7qBsaGMMrtKb4Ncm7DQQvQ9I7wPSx4ASvz3wpW9Ks_X_6z8txWN2</recordid><startdate>20210309</startdate><enddate>20210309</enddate><creator>Yang, Zhe</creator><creator>Ying, Shi</creator><creator>Wang, Bingming</creator><creator>Li, Yiyao</creator><creator>Dong, Bo</creator><creator>Geng, Jiangyi</creator><creator>Zhang, Ting</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-0471-0021</orcidid><orcidid>https://orcid.org/0000-0002-8723-0970</orcidid></search><sort><creationdate>20210309</creationdate><title>A System Fault Diagnosis Method with a Reclustering Algorithm</title><author>Yang, Zhe ; Ying, Shi ; Wang, Bingming ; Li, Yiyao ; Dong, Bo ; Geng, Jiangyi ; Zhang, Ting</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-bfb4d11eae3e7d62417a80ca8dc5232e4696484652e863f3c3c680ce57b96a153</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Clustering</topic><topic>Failure</topic><topic>Fault diagnosis</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Semantics</topic><topic>Software</topic><topic>Supercomputers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Zhe</creatorcontrib><creatorcontrib>Ying, Shi</creatorcontrib><creatorcontrib>Wang, Bingming</creatorcontrib><creatorcontrib>Li, Yiyao</creatorcontrib><creatorcontrib>Dong, Bo</creatorcontrib><creatorcontrib>Geng, Jiangyi</creatorcontrib><creatorcontrib>Zhang, Ting</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Scientific programming</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Zhe</au><au>Ying, Shi</au><au>Wang, Bingming</au><au>Li, Yiyao</au><au>Dong, Bo</au><au>Geng, Jiangyi</au><au>Zhang, Ting</au><au>Wang, Pengwei</au><au>Pengwei Wang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A System Fault Diagnosis Method with a Reclustering Algorithm</atitle><jtitle>Scientific programming</jtitle><date>2021-03-09</date><risdate>2021</risdate><volume>2021</volume><spage>1</spage><epage>8</epage><pages>1-8</pages><issn>1058-9244</issn><eissn>1875-919X</eissn><abstract>The log analysis-based system fault diagnosis method can help engineers analyze the fault events generated by the system. The K-means algorithm can perform log analysis well and does not require a lot of prior knowledge, but the K-means-based system fault diagnosis method needs to be improved in both efficiency and accuracy. To solve this problem, we propose a system fault diagnosis method based on a reclustering algorithm. First, we propose a log vectorization method based on the PV-DM language model to obtain low-dimensional log vectors which can provide effective data support for the subsequent fault diagnosis; then, we improve the K-means algorithm and make the effect of K-means algorithm based log clustering; finally, we propose a reclustering method based on keywords’ extraction to improve the accuracy of fault diagnosis. We use system log data generated by two supercomputers to verify our method. 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| source | Wiley Online Library Open Access; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Accuracy Algorithms Clustering Failure Fault diagnosis Machine learning Neural networks Semantics Software Supercomputers |
| title | A System Fault Diagnosis Method with a Reclustering Algorithm |
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