Bayesian Networks in Fault Diagnosis
Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibl...
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| Published in | IEEE transactions on industrial informatics Vol. 13; no. 5; pp. 2227 - 2240 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.10.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research. |
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| AbstractList | Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research. |
| Author | Min Xie Baoping Cai Lei Huang |
| Author_xml | – sequence: 1 givenname: Baoping surname: Cai fullname: Cai, Baoping – sequence: 2 givenname: Lei surname: Huang fullname: Huang, Lei – sequence: 3 givenname: Min surname: Xie fullname: Xie, Min |
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| CODEN | ITIICH |
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| Cites_doi | 10.1109/TSMCC.2013.2257752 10.1080/00207543.2011.611543 10.1016/S0957-4174(99)00054-8 10.1016/j.ymssp.2012.09.015 10.1007/s00170-016-8795-x 10.1111/j.1539-6924.2012.01918.x 10.1016/j.energy.2015.10.068 10.1016/j.chemolab.2014.07.009 10.1016/S0957-4174(98)00038-4 10.1109/TCST.2009.2026285 10.1016/j.ymssp.2011.03.006 10.1016/j.engappai.2011.02.018 10.1016/j.cie.2005.06.002 10.1021/ie503530v 10.1002/aic.14013 10.1016/j.engappai.2010.05.002 10.1109/TPWRD.2002.1022804 10.1016/j.ymssp.2015.12.020 10.1016/j.ress.2005.11.035 10.1109/TASE.2013.2287101 10.1007/978-3-642-04492-2_7 10.1109/TII.2012.2214394 10.1016/j.engappai.2010.06.002 10.1016/j.ress.2009.10.007 10.1016/j.oceaneng.2015.10.048 10.1016/S0098-1354(02)00161-8 10.1016/j.eswa.2011.02.171 10.1016/j.jprocont.2010.06.001 10.1109/TIE.2015.2417501 10.1109/TASE.2014.2321011 10.1016/j.jsv.2014.02.038 10.1016/j.knosys.2011.12.011 10.1016/j.applthermaleng.2015.09.121 10.1016/j.asoc.2010.02.019 10.1109/TPWRD.2005.858774 10.1109/TPEL.2016.2608842 10.1016/j.knosys.2014.10.012 10.1016/j.eswa.2014.06.029 10.1016/j.prevetmed.2009.02.009 10.1016/j.ijar.2014.02.005 10.1016/j.isatra.2015.06.011 10.1016/j.apenergy.2012.02.049 10.1109/TIE.2013.2261033 10.1016/j.nucengdes.2015.05.010 10.1016/j.eswa.2013.07.064 10.1007/s10845-008-0083-7 10.1016/j.engappai.2012.08.008 10.1016/j.compchemeng.2005.05.005 10.1080/0951192X.2013.812803 10.1007/s11668-016-0140-z 10.1016/j.ress.2014.10.021 10.1016/j.renene.2015.10.061 10.1109/TDEI.2014.004478 10.1016/j.ijmachtools.2004.06.018 10.1109/TSMCA.2010.2052037 10.3390/s16010076 10.1016/j.parco.2006.11.005 10.1016/0959-1524(96)00031-5 10.1016/j.rser.2016.04.030 10.1016/j.eswa.2006.08.011 10.1177/1077546314547533 10.1016/j.jprocont.2008.06.006 10.1016/j.ymssp.2011.10.018 10.1080/15732471003588387 10.1016/j.ress.2015.06.013 10.1613/jair.3232 10.1109/TII.2009.2033181 10.1016/j.ress.2006.09.012 10.1145/2818302 10.1016/j.engappai.2015.06.010 10.1002/9780470994559 10.1007/s10489-005-3413-x 10.1017/CBO9780511811357 10.1016/j.autcon.2013.10.019 10.1016/j.energy.2015.04.090 10.1109/TVT.2007.912610 10.1007/s00170-014-5918-0 10.1007/s00170-012-4252-7 10.1109/TSMCC.2012.2187188 10.1002/9780470117842 10.1016/j.ress.2012.07.006 10.1109/TFUZZ.2016.2587325 10.1109/TSMCC.2010.2049994 10.1016/j.ces.2011.07.025 10.1016/j.eswa.2007.09.030 10.1016/j.aap.2016.04.020 10.1016/j.apenergy.2013.09.043 10.1016/j.jprocont.2013.08.011 10.1016/j.comcom.2010.07.021 10.1016/S0167-8655(99)00090-2 10.1115/1.4032399 10.1016/j.measurement.2013.05.015 10.1016/j.jprocont.2015.06.004 10.1002/qre.978 10.1016/j.applthermaleng.2015.07.001 10.1016/j.trc.2008.04.001 10.1016/j.enbuild.2012.11.007 10.1016/j.ymssp.2013.02.016 10.1016/S0951-8320(00)00077-6 10.1016/j.asoc.2014.04.007 10.1016/j.ymssp.2011.10.001 10.1016/j.ress.2015.02.007 10.1016/j.ress.2005.11.037 10.1016/j.jmsy.2013.03.001 10.1016/j.jnca.2012.11.004 10.1016/j.jpowsour.2007.09.010 10.1111/risa.12112 10.1109/TIE.2014.2301773 10.1016/j.ymssp.2016.04.019 10.1109/TCST.2009.2020863 10.1016/j.applthermaleng.2016.06.153 10.1016/j.automatica.2011.02.015 10.1016/S0098-1354(02)00162-X 10.1016/j.cie.2016.01.007 10.1057/palgrave.jors.2602388 10.1016/j.sigpro.2013.04.015 10.1007/978-0-387-74101-7 10.1016/j.eswa.2014.10.020 10.19026/rjaset.6.3696 10.1016/j.jngse.2016.06.054 10.1109/TASE.2016.2574875 |
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| References | ref57 ref56 ref53 ref52 liu (ref59) 2012; 30 ref55 ref54 zhou (ref103) 2011 mcafee (ref126) 2012; 90 ref46 ref45 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref100 ref101 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref39 ref38 ref24 ref26 ref25 ref20 ref22 ref21 pourret (ref134) 2008 (ref132) 2012 ref28 ref27 ref29 ref13 ref12 ref128 ref15 ref129 ref14 ref127 ref124 ref99 ref11 ref125 ref98 ref10 ref17 ref16 ref19 ref18 ref133 ref93 ref92 ref131 ref95 ref94 ref130 ref91 ref90 ref89 lerner (ref86) 0 ref85 ref135 ref88 ref87 chiremsel (ref102) 2016; 16 pearl (ref23) 0 ref82 ref81 ref84 ref83 ref80 ref79 ref108 ref78 ref109 ref106 ref107 ref75 ref104 ref74 ref105 ref77 ref76 chiremsel (ref58) 2016; 16 nielsen (ref96) 2009 ref2 ref1 li (ref50) 2011; 40 ref71 ref111 ref70 ref112 ref72 ref110 ref68 ref119 kraaijeveld (ref51) 0 ref67 ref69 ref118 ref64 ref115 ref63 ref116 ref66 ref113 ref65 koller (ref97) 0 ref114 wang (ref117) 2013; 6 pearl (ref73) 2014 bartram (ref48) 2013 ref60 ref122 ref123 ref62 ref120 ref61 ref121 |
| References_xml | – start-page: 329 year: 0 ident: ref23 article-title: Bayesian networks: A model of self-activated memory for evidential reasoning publication-title: Proc 7th Annu Conf Cogn Sci Soc – ident: ref4 doi: 10.1109/TSMCC.2013.2257752 – ident: ref46 doi: 10.1080/00207543.2011.611543 – ident: ref84 doi: 10.1016/S0957-4174(99)00054-8 – ident: ref3 doi: 10.1016/j.ymssp.2012.09.015 – ident: ref72 doi: 10.1007/s00170-016-8795-x – ident: ref31 doi: 10.1111/j.1539-6924.2012.01918.x – ident: ref27 doi: 10.1016/j.energy.2015.10.068 – ident: ref93 doi: 10.1016/j.chemolab.2014.07.009 – ident: ref83 doi: 10.1016/S0957-4174(98)00038-4 – ident: ref2 doi: 10.1109/TCST.2009.2026285 – ident: ref15 doi: 10.1016/j.ymssp.2011.03.006 – ident: ref62 doi: 10.1016/j.engappai.2011.02.018 – ident: ref87 doi: 10.1016/j.cie.2005.06.002 – ident: ref70 doi: 10.1021/ie503530v – ident: ref92 doi: 10.1002/aic.14013 – ident: ref63 doi: 10.1016/j.chemolab.2014.07.009 – ident: ref66 doi: 10.1016/j.engappai.2010.05.002 – ident: ref79 doi: 10.1109/TPWRD.2002.1022804 – ident: ref18 doi: 10.1016/j.ymssp.2015.12.020 – ident: ref130 doi: 10.1016/j.ress.2005.11.035 – ident: ref112 doi: 10.1109/TASE.2013.2287101 – ident: ref125 doi: 10.1007/978-3-642-04492-2_7 – ident: ref14 doi: 10.1109/TII.2012.2214394 – ident: ref25 doi: 10.1016/j.engappai.2010.06.002 – ident: ref129 doi: 10.1016/j.ress.2009.10.007 – ident: ref35 doi: 10.1016/j.oceaneng.2015.10.048 – ident: ref1 doi: 10.1016/S0098-1354(02)00161-8 – ident: ref40 doi: 10.1016/j.eswa.2011.02.171 – ident: ref114 doi: 10.1016/j.jprocont.2010.06.001 – ident: ref9 doi: 10.1109/TIE.2015.2417501 – ident: ref60 doi: 10.1109/TASE.2014.2321011 – ident: ref16 doi: 10.1016/j.jsv.2014.02.038 – volume: 30 start-page: 95 year: 2012 ident: ref59 article-title: A search problem in complex diagnostic Bayesian networks publication-title: Knowl -Based Syst doi: 10.1016/j.knosys.2011.12.011 – ident: ref53 doi: 10.1016/j.applthermaleng.2015.09.121 – ident: ref45 doi: 10.1016/j.asoc.2010.02.019 – ident: ref71 doi: 10.1109/TPWRD.2005.858774 – ident: ref131 doi: 10.1109/TPEL.2016.2608842 – ident: ref104 doi: 10.1016/j.knosys.2014.10.012 – ident: ref32 doi: 10.1016/j.eswa.2014.06.029 – start-page: 175 year: 0 ident: ref51 article-title: Genierate: An interactive generator of diagnostic Bayesian network models publication-title: Proc 16th Int Workshop Principles Diagnosis – ident: ref99 doi: 10.1016/j.prevetmed.2009.02.009 – ident: ref127 doi: 10.1016/j.ijar.2014.02.005 – ident: ref28 doi: 10.1016/j.isatra.2015.06.011 – volume: 90 start-page: 60 year: 2012 ident: ref126 article-title: Big data: The management revolution publication-title: Harvard Bus Rev – ident: ref116 doi: 10.1016/j.apenergy.2012.02.049 – ident: ref20 doi: 10.1109/TIE.2013.2261033 – ident: ref94 doi: 10.1016/j.nucengdes.2015.05.010 – ident: ref82 doi: 10.1016/j.eswa.2013.07.064 – ident: ref65 doi: 10.1007/s10845-008-0083-7 – ident: ref91 doi: 10.1016/j.engappai.2012.08.008 – ident: ref98 doi: 10.1016/j.compchemeng.2005.05.005 – ident: ref123 doi: 10.1080/0951192X.2013.812803 – volume: 16 start-page: 747 year: 2016 ident: ref102 article-title: Probabilistic fault diagnosis of safety instrumented systems based on fault tree analysis and Bayesian network publication-title: Journal of Failure Analysis and Prevention doi: 10.1007/s11668-016-0140-z – ident: ref95 doi: 10.1016/j.ress.2014.10.021 – ident: ref118 doi: 10.1016/j.renene.2015.10.061 – ident: ref22 doi: 10.1109/TDEI.2014.004478 – ident: ref39 doi: 10.1016/j.ijmachtools.2004.06.018 – ident: ref121 doi: 10.1109/TSMCA.2010.2052037 – ident: ref108 doi: 10.3390/s16010076 – ident: ref107 doi: 10.1016/j.parco.2006.11.005 – ident: ref113 doi: 10.1016/0959-1524(96)00031-5 – ident: ref115 doi: 10.1016/j.rser.2016.04.030 – ident: ref21 doi: 10.1016/j.eswa.2006.08.011 – year: 2013 ident: ref48 article-title: System health diagnosis and prognosis using dynamic Bayesian networks – ident: ref12 doi: 10.1177/1077546314547533 – ident: ref81 doi: 10.1016/j.jprocont.2008.06.006 – ident: ref90 doi: 10.1016/j.ymssp.2011.10.018 – ident: ref122 doi: 10.1080/15732471003588387 – ident: ref29 doi: 10.1016/j.ress.2015.06.013 – volume: 40 start-page: 729 year: 2011 ident: ref50 article-title: Exploiting structure in weighted model counting approaches to probabilistic inference publication-title: J Artif Intell Res doi: 10.1613/jair.3232 – ident: ref13 doi: 10.1109/TII.2009.2033181 – ident: ref88 doi: 10.1016/j.ress.2006.09.012 – ident: ref105 doi: 10.1145/2818302 – ident: ref76 doi: 10.1016/j.engappai.2015.06.010 – year: 2008 ident: ref134 publication-title: Bayesian Networks A Practical Guide to Applications doi: 10.1002/9780470994559 – ident: ref85 doi: 10.1007/s10489-005-3413-x – start-page: 531 year: 0 ident: ref86 article-title: Bayesian fault detection and diagnosis in dynamic systems publication-title: Proc AAAI/IAAI – ident: ref38 doi: 10.1017/CBO9780511811357 – ident: ref52 doi: 10.1016/j.autcon.2013.10.019 – ident: ref119 doi: 10.1016/j.energy.2015.04.090 – ident: ref56 doi: 10.1109/TVT.2007.912610 – ident: ref69 doi: 10.1007/s00170-014-5918-0 – ident: ref111 doi: 10.1007/s00170-012-4252-7 – ident: ref74 doi: 10.1109/TSMCC.2012.2187188 – ident: ref135 doi: 10.1002/9780470117842 – ident: ref26 doi: 10.1016/j.ress.2012.07.006 – ident: ref109 doi: 10.1109/TFUZZ.2016.2587325 – ident: ref57 doi: 10.1109/TSMCC.2010.2049994 – ident: ref89 doi: 10.1016/j.ces.2011.07.025 – ident: ref100 doi: 10.1016/j.eswa.2007.09.030 – ident: ref33 doi: 10.1016/j.aap.2016.04.020 – start-page: 691 year: 2011 ident: ref103 article-title: Application of Bayesian network in failure diagnosis of hydro-electrical simulation system publication-title: Advances in Intelligent Decision Technologies – year: 2009 ident: ref96 publication-title: Bayesian Networks and Decision Graphs – ident: ref133 doi: 10.1017/CBO9780511811357 – ident: ref42 doi: 10.1016/j.apenergy.2013.09.043 – year: 2014 ident: ref73 publication-title: Probabilistic Reasoning in Intelligent Systems Networks of Plausible Inference – ident: ref8 doi: 10.1016/j.jprocont.2013.08.011 – ident: ref47 doi: 10.1016/j.comcom.2010.07.021 – ident: ref61 doi: 10.1016/S0167-8655(99)00090-2 – ident: ref34 doi: 10.1115/1.4032399 – ident: ref17 doi: 10.1016/j.measurement.2013.05.015 – ident: ref11 doi: 10.1016/j.jprocont.2015.06.004 – ident: ref44 doi: 10.1002/qre.978 – ident: ref78 doi: 10.1016/j.applthermaleng.2015.07.001 – ident: ref54 doi: 10.1016/j.trc.2008.04.001 – ident: ref41 doi: 10.1016/j.enbuild.2012.11.007 – ident: ref19 doi: 10.1016/j.ymssp.2013.02.016 – ident: ref37 doi: 10.1016/S0951-8320(00)00077-6 – ident: ref110 doi: 10.1016/j.asoc.2014.04.007 – start-page: 302 year: 0 ident: ref97 article-title: Object-oriented Bayesian networks publication-title: Proc 13th Conf Uncertainty Artif Intell – ident: ref80 doi: 10.1016/j.ymssp.2011.10.001 – ident: ref30 doi: 10.1016/j.ress.2015.02.007 – ident: ref24 doi: 10.1016/j.ress.2005.11.037 – ident: ref101 doi: 10.1016/j.jmsy.2013.03.001 – ident: ref124 doi: 10.1016/j.jnca.2012.11.004 – year: 2012 ident: ref132 publication-title: Assessing the Reliability of Complex Models Mathematical and Statistical Foundations of Verification Validation and Uncertainty Quantification – volume: 16 start-page: 747 year: 2016 ident: ref58 article-title: Probabilistic fault diagnosis of safety instrumented systems based on fault tree analysis and Bayesian network publication-title: Journal of Failure Analysis and Prevention doi: 10.1007/s11668-016-0140-z – ident: ref10 doi: 10.1109/TIE.2015.2417501 – ident: ref120 doi: 10.1016/j.jpowsour.2007.09.010 – ident: ref77 doi: 10.1111/risa.12112 – ident: ref7 doi: 10.1109/TIE.2014.2301773 – ident: ref49 doi: 10.1016/j.ymssp.2016.04.019 – ident: ref106 doi: 10.1109/TCST.2009.2020863 – ident: ref128 doi: 10.1016/j.applthermaleng.2016.06.153 – ident: ref68 doi: 10.1016/j.automatica.2011.02.015 – ident: ref6 doi: 10.1016/S0098-1354(02)00162-X – ident: ref36 doi: 10.1016/j.cie.2016.01.007 – ident: ref67 doi: 10.1057/palgrave.jors.2602388 – ident: ref5 doi: 10.1016/j.sigpro.2013.04.015 – ident: ref75 doi: 10.1007/978-0-387-74101-7 – ident: ref43 doi: 10.1016/j.eswa.2014.10.020 – volume: 6 start-page: 3065 year: 2013 ident: ref117 article-title: Research on wind turbine generator dynamic reliability test system based on feature recognition publication-title: Res J Appl Sci Eng Technol doi: 10.19026/rjaset.6.3696 – ident: ref64 doi: 10.1016/j.jngse.2016.06.054 – ident: ref55 doi: 10.1109/TASE.2016.2574875 |
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| SubjectTerms | Analytical models Bayesian analysis Bayesian networks (BNs) Fault detection Fault diagnosis Inference algorithms Learning systems Maintenance Mathematical model Modelling Probability |
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