Design of an artificial immune system based on Danger Model for fault detection
This paper presents a methodology that enables fault detection in dynamic systems based on recent immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The fault detection central challenge is de...
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Veröffentlicht in: | Expert systems with applications 2010-07, Vol.37 (7), p.5145-5152 |
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creator | Laurentys, C.A. Palhares, R.M. Caminhas, W.M. |
description | This paper presents a methodology that enables fault detection in dynamic systems based on recent immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The fault detection central challenge is determining the difference between normal and potential harmful activities at dynamic systems. A promising solution is emerging in the form of Artificial Immune Systems (AIS). The Danger Model (DM) proposes that the immune system reacts not against self or non-self but by threats generated into the organism: the danger signals. DM-based fault detection system proposes a new formulation for a fault detection system. A DM-inspired methodology is applied to a fault detection benchmark provided by DAMADICS to compare its relative performance to others algorithms. The results show that the strategy developed is promising for incipient and abrupt fault detection in dynamic systems. |
doi_str_mv | 10.1016/j.eswa.2009.12.079 |
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The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The fault detection central challenge is determining the difference between normal and potential harmful activities at dynamic systems. A promising solution is emerging in the form of Artificial Immune Systems (AIS). The Danger Model (DM) proposes that the immune system reacts not against self or non-self but by threats generated into the organism: the danger signals. DM-based fault detection system proposes a new formulation for a fault detection system. A DM-inspired methodology is applied to a fault detection benchmark provided by DAMADICS to compare its relative performance to others algorithms. The results show that the strategy developed is promising for incipient and abrupt fault detection in dynamic systems.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2009.12.079</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Artificial immune system ; Computational intelligence ; Decision support ; Fault detection ; Fuzzy set ; Model development ; Neural network</subject><ispartof>Expert systems with applications, 2010-07, Vol.37 (7), p.5145-5152</ispartof><rights>2009 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c332t-ab97ecf3562fd4c76869f86b804a18f7f0e87df7f4a4d4f058763f9aaf700f013</citedby><cites>FETCH-LOGICAL-c332t-ab97ecf3562fd4c76869f86b804a18f7f0e87df7f4a4d4f058763f9aaf700f013</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.eswa.2009.12.079$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Laurentys, C.A.</creatorcontrib><creatorcontrib>Palhares, R.M.</creatorcontrib><creatorcontrib>Caminhas, W.M.</creatorcontrib><title>Design of an artificial immune system based on Danger Model for fault detection</title><title>Expert systems with applications</title><description>This paper presents a methodology that enables fault detection in dynamic systems based on recent immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The fault detection central challenge is determining the difference between normal and potential harmful activities at dynamic systems. A promising solution is emerging in the form of Artificial Immune Systems (AIS). The Danger Model (DM) proposes that the immune system reacts not against self or non-self but by threats generated into the organism: the danger signals. DM-based fault detection system proposes a new formulation for a fault detection system. A DM-inspired methodology is applied to a fault detection benchmark provided by DAMADICS to compare its relative performance to others algorithms. The results show that the strategy developed is promising for incipient and abrupt fault detection in dynamic systems.</description><subject>Artificial immune system</subject><subject>Computational intelligence</subject><subject>Decision support</subject><subject>Fault detection</subject><subject>Fuzzy set</subject><subject>Model development</subject><subject>Neural network</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNp9kD1PwzAURS0EEqXwB5i8MSXYjms7Egtq-ZKKusBsuc5z5Sqxi52A-u9JVGamu9zz9O5B6JaSkhIq7vcl5B9TMkLqkrKSyPoMzaiSVSFkXZ2jGakXsuBU8kt0lfOeECoJkTO0WUH2u4CjwyZgk3rvvPWmxb7rhgA4H3MPHd6aDA2OAa9M2EHC77GBFruYsDND2-MGerC9j-EaXTjTZrj5yzn6fH76WL4W683L2_JxXdiqYn1htrUE66qFYK7hVgolaqfEVhFuqHLSEVCyGZMb3nBHFkqKytXGuPFtR2g1R3enu4cUvwbIve58ttC2JkAcspZcMKaYmprs1LQp5pzA6UPynUlHTYme5Om9nuTpSZ6mTI_yRujhBMG44dtD0tl6CBYan8ahuon-P_wXKbZ4kA</recordid><startdate>20100701</startdate><enddate>20100701</enddate><creator>Laurentys, C.A.</creator><creator>Palhares, R.M.</creator><creator>Caminhas, W.M.</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7T5</scope><scope>H94</scope></search><sort><creationdate>20100701</creationdate><title>Design of an artificial immune system based on Danger Model for fault detection</title><author>Laurentys, C.A. ; Palhares, R.M. ; Caminhas, W.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c332t-ab97ecf3562fd4c76869f86b804a18f7f0e87df7f4a4d4f058763f9aaf700f013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Artificial immune system</topic><topic>Computational intelligence</topic><topic>Decision support</topic><topic>Fault detection</topic><topic>Fuzzy set</topic><topic>Model development</topic><topic>Neural network</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Laurentys, C.A.</creatorcontrib><creatorcontrib>Palhares, R.M.</creatorcontrib><creatorcontrib>Caminhas, W.M.</creatorcontrib><collection>CrossRef</collection><collection>Immunology Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Laurentys, C.A.</au><au>Palhares, R.M.</au><au>Caminhas, W.M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Design of an artificial immune system based on Danger Model for fault detection</atitle><jtitle>Expert systems with applications</jtitle><date>2010-07-01</date><risdate>2010</risdate><volume>37</volume><issue>7</issue><spage>5145</spage><epage>5152</epage><pages>5145-5152</pages><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>This paper presents a methodology that enables fault detection in dynamic systems based on recent immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The fault detection central challenge is determining the difference between normal and potential harmful activities at dynamic systems. A promising solution is emerging in the form of Artificial Immune Systems (AIS). The Danger Model (DM) proposes that the immune system reacts not against self or non-self but by threats generated into the organism: the danger signals. DM-based fault detection system proposes a new formulation for a fault detection system. A DM-inspired methodology is applied to a fault detection benchmark provided by DAMADICS to compare its relative performance to others algorithms. The results show that the strategy developed is promising for incipient and abrupt fault detection in dynamic systems.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2009.12.079</doi><tpages>8</tpages></addata></record> |
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subjects | Artificial immune system Computational intelligence Decision support Fault detection Fuzzy set Model development Neural network |
title | Design of an artificial immune system based on Danger Model for fault detection |
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