Sensitivity analysis by differential importance measure for unsupervised fault diagnostics

Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel...
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Autor*in:	Floreale, Giovanni [verfasserIn] Baraldi, Piero [verfasserIn] Lu, Xuefei [verfasserIn] Rossetti, Paolo [verfasserIn] Zio, Enrico [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Condition monitoring Fault diagnostics Sensitivity analysis Differential Importance Measure Wind turbine

Übergeordnetes Werk:	Enthalten in: Reliability engineering & system safety - London [u.a.] : Elsevier Science, 1988, 243
Übergeordnetes Werk:	volume:243

DOI / URN:	10.1016/j.ress.2023.109846

Katalog-ID:	ELV066430526

Internformat


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520			\|a Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique.
650		4	\|a Condition monitoring
650		4	\|a Fault diagnostics
650		4	\|a Sensitivity analysis
650		4	\|a Differential Importance Measure
650		4	\|a Wind turbine
700	1		\|a Baraldi, Piero \|e verfasserin \|0 (orcid)0000-0003-4232-4161 \|4 aut
700	1		\|a Lu, Xuefei \|e verfasserin \|0 (orcid)0000-0003-2103-6478 \|4 aut
700	1		\|a Rossetti, Paolo \|e verfasserin \|4 aut
700	1		\|a Zio, Enrico \|e verfasserin \|0 (orcid)0000-0002-7108-637X \|4 aut
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936	b	k	\|a 50.16 \|j Technische Zuverlässigkeit \|j Instandhaltung \|q VZ
936	b	k	\|a 85.38 \|j Qualitätsmanagement \|q VZ
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bklnumber	50.16 85.38
publishDate	2023
allfields	10.1016/j.ress.2023.109846 doi (DE-627)ELV066430526 (ELSEVIER)S0951-8320(23)00760-3 DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Floreale, Giovanni verfasserin (orcid)0000-0003-3126-0414 aut Sensitivity analysis by differential importance measure for unsupervised fault diagnostics 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique. Condition monitoring Fault diagnostics Sensitivity analysis Differential Importance Measure Wind turbine Baraldi, Piero verfasserin (orcid)0000-0003-4232-4161 aut Lu, Xuefei verfasserin (orcid)0000-0003-2103-6478 aut Rossetti, Paolo verfasserin aut Zio, Enrico verfasserin (orcid)0000-0002-7108-637X aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 243 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 243
spelling	10.1016/j.ress.2023.109846 doi (DE-627)ELV066430526 (ELSEVIER)S0951-8320(23)00760-3 DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Floreale, Giovanni verfasserin (orcid)0000-0003-3126-0414 aut Sensitivity analysis by differential importance measure for unsupervised fault diagnostics 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique. Condition monitoring Fault diagnostics Sensitivity analysis Differential Importance Measure Wind turbine Baraldi, Piero verfasserin (orcid)0000-0003-4232-4161 aut Lu, Xuefei verfasserin (orcid)0000-0003-2103-6478 aut Rossetti, Paolo verfasserin aut Zio, Enrico verfasserin (orcid)0000-0002-7108-637X aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 243 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 243
allfields_unstemmed	10.1016/j.ress.2023.109846 doi (DE-627)ELV066430526 (ELSEVIER)S0951-8320(23)00760-3 DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Floreale, Giovanni verfasserin (orcid)0000-0003-3126-0414 aut Sensitivity analysis by differential importance measure for unsupervised fault diagnostics 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique. Condition monitoring Fault diagnostics Sensitivity analysis Differential Importance Measure Wind turbine Baraldi, Piero verfasserin (orcid)0000-0003-4232-4161 aut Lu, Xuefei verfasserin (orcid)0000-0003-2103-6478 aut Rossetti, Paolo verfasserin aut Zio, Enrico verfasserin (orcid)0000-0002-7108-637X aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 243 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 243
allfieldsGer	10.1016/j.ress.2023.109846 doi (DE-627)ELV066430526 (ELSEVIER)S0951-8320(23)00760-3 DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Floreale, Giovanni verfasserin (orcid)0000-0003-3126-0414 aut Sensitivity analysis by differential importance measure for unsupervised fault diagnostics 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique. Condition monitoring Fault diagnostics Sensitivity analysis Differential Importance Measure Wind turbine Baraldi, Piero verfasserin (orcid)0000-0003-4232-4161 aut Lu, Xuefei verfasserin (orcid)0000-0003-2103-6478 aut Rossetti, Paolo verfasserin aut Zio, Enrico verfasserin (orcid)0000-0002-7108-637X aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 243 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 243
allfieldsSound	10.1016/j.ress.2023.109846 doi (DE-627)ELV066430526 (ELSEVIER)S0951-8320(23)00760-3 DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Floreale, Giovanni verfasserin (orcid)0000-0003-3126-0414 aut Sensitivity analysis by differential importance measure for unsupervised fault diagnostics 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique. Condition monitoring Fault diagnostics Sensitivity analysis Differential Importance Measure Wind turbine Baraldi, Piero verfasserin (orcid)0000-0003-4232-4161 aut Lu, Xuefei verfasserin (orcid)0000-0003-2103-6478 aut Rossetti, Paolo verfasserin aut Zio, Enrico verfasserin (orcid)0000-0002-7108-637X aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 243 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 243
language	English
source	Enthalten in Reliability engineering & system safety 243 volume:243
sourceStr	Enthalten in Reliability engineering & system safety 243 volume:243
format_phy_str_mv	Article
bklname	Technische Zuverlässigkeit Instandhaltung Qualitätsmanagement
institution	findex.gbv.de
topic_facet	Condition monitoring Fault diagnostics Sensitivity analysis Differential Importance Measure Wind turbine
dewey-raw	600
isfreeaccess_bool	false
container_title	Reliability engineering & system safety
authorswithroles_txt_mv	Floreale, Giovanni @@aut@@ Baraldi, Piero @@aut@@ Lu, Xuefei @@aut@@ Rossetti, Paolo @@aut@@ Zio, Enrico @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320608743
dewey-sort	3600
id	ELV066430526
language_de	englisch
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author	Floreale, Giovanni
spellingShingle	Floreale, Giovanni ddc 600 bkl 50.16 bkl 85.38 misc Condition monitoring misc Fault diagnostics misc Sensitivity analysis misc Differential Importance Measure misc Wind turbine Sensitivity analysis by differential importance measure for unsupervised fault diagnostics
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topic_title	600 VZ 50.16 bkl 85.38 bkl Sensitivity analysis by differential importance measure for unsupervised fault diagnostics Condition monitoring Fault diagnostics Sensitivity analysis Differential Importance Measure Wind turbine
topic	ddc 600 bkl 50.16 bkl 85.38 misc Condition monitoring misc Fault diagnostics misc Sensitivity analysis misc Differential Importance Measure misc Wind turbine
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title	Sensitivity analysis by differential importance measure for unsupervised fault diagnostics
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title_full	Sensitivity analysis by differential importance measure for unsupervised fault diagnostics
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title_sort	sensitivity analysis by differential importance measure for unsupervised fault diagnostics
title_auth	Sensitivity analysis by differential importance measure for unsupervised fault diagnostics
abstract	Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique.
abstractGer	Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique.
abstract_unstemmed	Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique.
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title_short	Sensitivity analysis by differential importance measure for unsupervised fault diagnostics
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author2	Baraldi, Piero Lu, Xuefei Rossetti, Paolo Zio, Enrico
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up_date	2024-07-06T17:44:02.422Z
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Sensitivity analysis by differential importance measure for unsupervised fault diagnostics

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