Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter

This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failu...
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Autor*in:	András Darányi [verfasserIn] János Abonyi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	fault diagnostics particle filter monitoring sensor fault actuator fault

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 24(2024), 3, p 719
Übergeordnetes Werk:	volume:24 ; year:2024 ; number:3, p 719

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DOI / URN:	10.3390/s24030719

Katalog-ID:	DOAJ094474710

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spelling	10.3390/s24030719 doi (DE-627)DOAJ094474710 (DE-599)DOAJ75cd81cb5ee64d5ba10632311863e57d DE-627 ger DE-627 rakwb eng TP1-1185 András Darányi verfasserin aut Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated. fault diagnostics particle filter monitoring sensor fault actuator fault Chemical technology János Abonyi verfasserin aut In Sensors MDPI AG, 2003 24(2024), 3, p 719 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2024 number:3, p 719 https://doi.org/10.3390/s24030719 kostenfrei https://doaj.org/article/75cd81cb5ee64d5ba10632311863e57d kostenfrei https://www.mdpi.com/1424-8220/24/3/719 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 3, p 719
allfields_unstemmed	10.3390/s24030719 doi (DE-627)DOAJ094474710 (DE-599)DOAJ75cd81cb5ee64d5ba10632311863e57d DE-627 ger DE-627 rakwb eng TP1-1185 András Darányi verfasserin aut Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated. fault diagnostics particle filter monitoring sensor fault actuator fault Chemical technology János Abonyi verfasserin aut In Sensors MDPI AG, 2003 24(2024), 3, p 719 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2024 number:3, p 719 https://doi.org/10.3390/s24030719 kostenfrei https://doaj.org/article/75cd81cb5ee64d5ba10632311863e57d kostenfrei https://www.mdpi.com/1424-8220/24/3/719 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 3, p 719
allfieldsGer	10.3390/s24030719 doi (DE-627)DOAJ094474710 (DE-599)DOAJ75cd81cb5ee64d5ba10632311863e57d DE-627 ger DE-627 rakwb eng TP1-1185 András Darányi verfasserin aut Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated. fault diagnostics particle filter monitoring sensor fault actuator fault Chemical technology János Abonyi verfasserin aut In Sensors MDPI AG, 2003 24(2024), 3, p 719 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2024 number:3, p 719 https://doi.org/10.3390/s24030719 kostenfrei https://doaj.org/article/75cd81cb5ee64d5ba10632311863e57d kostenfrei https://www.mdpi.com/1424-8220/24/3/719 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 3, p 719
allfieldsSound	10.3390/s24030719 doi (DE-627)DOAJ094474710 (DE-599)DOAJ75cd81cb5ee64d5ba10632311863e57d DE-627 ger DE-627 rakwb eng TP1-1185 András Darányi verfasserin aut Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated. fault diagnostics particle filter monitoring sensor fault actuator fault Chemical technology János Abonyi verfasserin aut In Sensors MDPI AG, 2003 24(2024), 3, p 719 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2024 number:3, p 719 https://doi.org/10.3390/s24030719 kostenfrei https://doaj.org/article/75cd81cb5ee64d5ba10632311863e57d kostenfrei https://www.mdpi.com/1424-8220/24/3/719 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 3, p 719
language	English
source	In Sensors 24(2024), 3, p 719 volume:24 year:2024 number:3, p 719
sourceStr	In Sensors 24(2024), 3, p 719 volume:24 year:2024 number:3, p 719
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	fault diagnostics particle filter monitoring sensor fault actuator fault Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	András Darányi @@aut@@ János Abonyi @@aut@@
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callnumber-first	T - Technology
author	András Darányi
spellingShingle	András Darányi misc TP1-1185 misc fault diagnostics misc particle filter misc monitoring misc sensor fault misc actuator fault misc Chemical technology Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter
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topic_title	TP1-1185 Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter fault diagnostics particle filter monitoring sensor fault actuator fault
topic	misc TP1-1185 misc fault diagnostics misc particle filter misc monitoring misc sensor fault misc actuator fault misc Chemical technology
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title_auth	Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter
abstract	This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated.
abstractGer	This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated.
abstract_unstemmed	This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated.
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title_short	Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter
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