Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design

System states are related, directly or indirectly, to health condition indicators. Indeed, critical system failures can be efficiently characterized through a state space manifold. This fact has encouraged the development of a series of failure prognostic frameworks based on Bayesian processors (e.g...
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Autor*in:	Acuña, David E. [verfasserIn] Orchard, Marcos E. [verfasserIn] Saona, Raimundo J. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Prognostics and health management Prognostic algorithm design Bayesian Cramér-Rao Lower Bounds Particle filters Battery end-of- discharge

Übergeordnetes Werk:	Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 72, Seite 647-665
Übergeordnetes Werk:	volume:72 ; pages:647-665

DOI / URN:	10.1016/j.asoc.2018.01.033

Katalog-ID:	ELV001014021

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520			\|a System states are related, directly or indirectly, to health condition indicators. Indeed, critical system failures can be efficiently characterized through a state space manifold. This fact has encouraged the development of a series of failure prognostic frameworks based on Bayesian processors (e.g. particle or unscented Kalman filters), which efficiently help to estimate the Time-of-Failure (ToF) probability distribution in nonlinear, non- Gaussian, systems with uncertain future operating profiles. However, it is still unclear how to determine the efficacy of these methods, since the Prognostics and Health Management (PHM) community has not developed rigorous theoretical frameworks that could help to define proper performance indicators. In this regard, this article introduces novel prognostic performance metric based on the concept of Bayesian Cramér-Rao Lower Bounds (BCRLBs) for the predicted state mean square error (MSE), which is conditional to measurement data and model dynamics; providing a formal mathematical definition of the prognostic problem. Furthermore, we propose a novel step-by-step design methodology to tune prognostic algorithm hyper-parameters, which allows to guarantee that obtained results do not violate fundamental precision bounds. As an illustrative example, both the predictive BCRLB concept and the proposed design methodology are applied to the problem of End-of-Discharge (EoD) time prognostics in lithium-ion batteries.
650		4	\|a Prognostics and health management
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700	1		\|a Saona, Raimundo J. \|e verfasserin \|4 aut
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author_variant	d e a de dea m e o me meo r j s rj rjs
matchkey_str	article:15684946:2018----::odtoapeitvbysacaralwronsopon
hierarchy_sort_str	2018
bklnumber	54.00
publishDate	2018
allfields	10.1016/j.asoc.2018.01.033 doi (DE-627)ELV001014021 (ELSEVIER)S1568-4946(18)30039-5 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Acuña, David E. verfasserin aut Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier System states are related, directly or indirectly, to health condition indicators. Indeed, critical system failures can be efficiently characterized through a state space manifold. This fact has encouraged the development of a series of failure prognostic frameworks based on Bayesian processors (e.g. particle or unscented Kalman filters), which efficiently help to estimate the Time-of-Failure (ToF) probability distribution in nonlinear, non- Gaussian, systems with uncertain future operating profiles. However, it is still unclear how to determine the efficacy of these methods, since the Prognostics and Health Management (PHM) community has not developed rigorous theoretical frameworks that could help to define proper performance indicators. In this regard, this article introduces novel prognostic performance metric based on the concept of Bayesian Cramér-Rao Lower Bounds (BCRLBs) for the predicted state mean square error (MSE), which is conditional to measurement data and model dynamics; providing a formal mathematical definition of the prognostic problem. Furthermore, we propose a novel step-by-step design methodology to tune prognostic algorithm hyper-parameters, which allows to guarantee that obtained results do not violate fundamental precision bounds. As an illustrative example, both the predictive BCRLB concept and the proposed design methodology are applied to the problem of End-of-Discharge (EoD) time prognostics in lithium-ion batteries. Prognostics and health management Prognostic algorithm design Bayesian Cramér-Rao Lower Bounds Particle filters Battery end-of- discharge Orchard, Marcos E. verfasserin aut Saona, Raimundo J. verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 72, Seite 647-665 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:72 pages:647-665 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 72 647-665
spelling	10.1016/j.asoc.2018.01.033 doi (DE-627)ELV001014021 (ELSEVIER)S1568-4946(18)30039-5 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Acuña, David E. verfasserin aut Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier System states are related, directly or indirectly, to health condition indicators. Indeed, critical system failures can be efficiently characterized through a state space manifold. This fact has encouraged the development of a series of failure prognostic frameworks based on Bayesian processors (e.g. particle or unscented Kalman filters), which efficiently help to estimate the Time-of-Failure (ToF) probability distribution in nonlinear, non- Gaussian, systems with uncertain future operating profiles. However, it is still unclear how to determine the efficacy of these methods, since the Prognostics and Health Management (PHM) community has not developed rigorous theoretical frameworks that could help to define proper performance indicators. In this regard, this article introduces novel prognostic performance metric based on the concept of Bayesian Cramér-Rao Lower Bounds (BCRLBs) for the predicted state mean square error (MSE), which is conditional to measurement data and model dynamics; providing a formal mathematical definition of the prognostic problem. Furthermore, we propose a novel step-by-step design methodology to tune prognostic algorithm hyper-parameters, which allows to guarantee that obtained results do not violate fundamental precision bounds. As an illustrative example, both the predictive BCRLB concept and the proposed design methodology are applied to the problem of End-of-Discharge (EoD) time prognostics in lithium-ion batteries. Prognostics and health management Prognostic algorithm design Bayesian Cramér-Rao Lower Bounds Particle filters Battery end-of- discharge Orchard, Marcos E. verfasserin aut Saona, Raimundo J. verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 72, Seite 647-665 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:72 pages:647-665 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 72 647-665
allfields_unstemmed	10.1016/j.asoc.2018.01.033 doi (DE-627)ELV001014021 (ELSEVIER)S1568-4946(18)30039-5 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Acuña, David E. verfasserin aut Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier System states are related, directly or indirectly, to health condition indicators. Indeed, critical system failures can be efficiently characterized through a state space manifold. This fact has encouraged the development of a series of failure prognostic frameworks based on Bayesian processors (e.g. particle or unscented Kalman filters), which efficiently help to estimate the Time-of-Failure (ToF) probability distribution in nonlinear, non- Gaussian, systems with uncertain future operating profiles. However, it is still unclear how to determine the efficacy of these methods, since the Prognostics and Health Management (PHM) community has not developed rigorous theoretical frameworks that could help to define proper performance indicators. In this regard, this article introduces novel prognostic performance metric based on the concept of Bayesian Cramér-Rao Lower Bounds (BCRLBs) for the predicted state mean square error (MSE), which is conditional to measurement data and model dynamics; providing a formal mathematical definition of the prognostic problem. Furthermore, we propose a novel step-by-step design methodology to tune prognostic algorithm hyper-parameters, which allows to guarantee that obtained results do not violate fundamental precision bounds. As an illustrative example, both the predictive BCRLB concept and the proposed design methodology are applied to the problem of End-of-Discharge (EoD) time prognostics in lithium-ion batteries. Prognostics and health management Prognostic algorithm design Bayesian Cramér-Rao Lower Bounds Particle filters Battery end-of- discharge Orchard, Marcos E. verfasserin aut Saona, Raimundo J. verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 72, Seite 647-665 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:72 pages:647-665 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 72 647-665
allfieldsGer	10.1016/j.asoc.2018.01.033 doi (DE-627)ELV001014021 (ELSEVIER)S1568-4946(18)30039-5 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Acuña, David E. verfasserin aut Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier System states are related, directly or indirectly, to health condition indicators. Indeed, critical system failures can be efficiently characterized through a state space manifold. This fact has encouraged the development of a series of failure prognostic frameworks based on Bayesian processors (e.g. particle or unscented Kalman filters), which efficiently help to estimate the Time-of-Failure (ToF) probability distribution in nonlinear, non- Gaussian, systems with uncertain future operating profiles. However, it is still unclear how to determine the efficacy of these methods, since the Prognostics and Health Management (PHM) community has not developed rigorous theoretical frameworks that could help to define proper performance indicators. In this regard, this article introduces novel prognostic performance metric based on the concept of Bayesian Cramér-Rao Lower Bounds (BCRLBs) for the predicted state mean square error (MSE), which is conditional to measurement data and model dynamics; providing a formal mathematical definition of the prognostic problem. Furthermore, we propose a novel step-by-step design methodology to tune prognostic algorithm hyper-parameters, which allows to guarantee that obtained results do not violate fundamental precision bounds. As an illustrative example, both the predictive BCRLB concept and the proposed design methodology are applied to the problem of End-of-Discharge (EoD) time prognostics in lithium-ion batteries. Prognostics and health management Prognostic algorithm design Bayesian Cramér-Rao Lower Bounds Particle filters Battery end-of- discharge Orchard, Marcos E. verfasserin aut Saona, Raimundo J. verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 72, Seite 647-665 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:72 pages:647-665 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 72 647-665
allfieldsSound	10.1016/j.asoc.2018.01.033 doi (DE-627)ELV001014021 (ELSEVIER)S1568-4946(18)30039-5 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Acuña, David E. verfasserin aut Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier System states are related, directly or indirectly, to health condition indicators. Indeed, critical system failures can be efficiently characterized through a state space manifold. This fact has encouraged the development of a series of failure prognostic frameworks based on Bayesian processors (e.g. particle or unscented Kalman filters), which efficiently help to estimate the Time-of-Failure (ToF) probability distribution in nonlinear, non- Gaussian, systems with uncertain future operating profiles. However, it is still unclear how to determine the efficacy of these methods, since the Prognostics and Health Management (PHM) community has not developed rigorous theoretical frameworks that could help to define proper performance indicators. In this regard, this article introduces novel prognostic performance metric based on the concept of Bayesian Cramér-Rao Lower Bounds (BCRLBs) for the predicted state mean square error (MSE), which is conditional to measurement data and model dynamics; providing a formal mathematical definition of the prognostic problem. Furthermore, we propose a novel step-by-step design methodology to tune prognostic algorithm hyper-parameters, which allows to guarantee that obtained results do not violate fundamental precision bounds. As an illustrative example, both the predictive BCRLB concept and the proposed design methodology are applied to the problem of End-of-Discharge (EoD) time prognostics in lithium-ion batteries. Prognostics and health management Prognostic algorithm design Bayesian Cramér-Rao Lower Bounds Particle filters Battery end-of- discharge Orchard, Marcos E. verfasserin aut Saona, Raimundo J. verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 72, Seite 647-665 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:72 pages:647-665 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 72 647-665
language	English
source	Enthalten in Applied soft computing 72, Seite 647-665 volume:72 pages:647-665
sourceStr	Enthalten in Applied soft computing 72, Seite 647-665 volume:72 pages:647-665
format_phy_str_mv	Article
bklname	Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Prognostics and health management Prognostic algorithm design Bayesian Cramér-Rao Lower Bounds Particle filters Battery end-of- discharge
dewey-raw	004
isfreeaccess_bool	false
container_title	Applied soft computing
authorswithroles_txt_mv	Acuña, David E. @@aut@@ Orchard, Marcos E. @@aut@@ Saona, Raimundo J. @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	334375754
dewey-sort	14
id	ELV001014021
language_de	englisch
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author	Acuña, David E.
spellingShingle	Acuña, David E. ddc 004 bkl 54.00 misc Prognostics and health management misc Prognostic algorithm design misc Bayesian Cramér-Rao Lower Bounds misc Particle filters misc Battery end-of- discharge Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design
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topic_title	004 DE-600 54.00 bkl Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design Prognostics and health management Prognostic algorithm design Bayesian Cramér-Rao Lower Bounds Particle filters Battery end-of- discharge
topic	ddc 004 bkl 54.00 misc Prognostics and health management misc Prognostic algorithm design misc Bayesian Cramér-Rao Lower Bounds misc Particle filters misc Battery end-of- discharge
topic_unstemmed	ddc 004 bkl 54.00 misc Prognostics and health management misc Prognostic algorithm design misc Bayesian Cramér-Rao Lower Bounds misc Particle filters misc Battery end-of- discharge
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title	Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design
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title_full	Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design
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title_sort	conditional predictive bayesian cramér-rao lower bounds for prognostic algorithms design
title_auth	Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design
abstract	System states are related, directly or indirectly, to health condition indicators. Indeed, critical system failures can be efficiently characterized through a state space manifold. This fact has encouraged the development of a series of failure prognostic frameworks based on Bayesian processors (e.g. particle or unscented Kalman filters), which efficiently help to estimate the Time-of-Failure (ToF) probability distribution in nonlinear, non- Gaussian, systems with uncertain future operating profiles. However, it is still unclear how to determine the efficacy of these methods, since the Prognostics and Health Management (PHM) community has not developed rigorous theoretical frameworks that could help to define proper performance indicators. In this regard, this article introduces novel prognostic performance metric based on the concept of Bayesian Cramér-Rao Lower Bounds (BCRLBs) for the predicted state mean square error (MSE), which is conditional to measurement data and model dynamics; providing a formal mathematical definition of the prognostic problem. Furthermore, we propose a novel step-by-step design methodology to tune prognostic algorithm hyper-parameters, which allows to guarantee that obtained results do not violate fundamental precision bounds. As an illustrative example, both the predictive BCRLB concept and the proposed design methodology are applied to the problem of End-of-Discharge (EoD) time prognostics in lithium-ion batteries.
abstractGer	System states are related, directly or indirectly, to health condition indicators. Indeed, critical system failures can be efficiently characterized through a state space manifold. This fact has encouraged the development of a series of failure prognostic frameworks based on Bayesian processors (e.g. particle or unscented Kalman filters), which efficiently help to estimate the Time-of-Failure (ToF) probability distribution in nonlinear, non- Gaussian, systems with uncertain future operating profiles. However, it is still unclear how to determine the efficacy of these methods, since the Prognostics and Health Management (PHM) community has not developed rigorous theoretical frameworks that could help to define proper performance indicators. In this regard, this article introduces novel prognostic performance metric based on the concept of Bayesian Cramér-Rao Lower Bounds (BCRLBs) for the predicted state mean square error (MSE), which is conditional to measurement data and model dynamics; providing a formal mathematical definition of the prognostic problem. Furthermore, we propose a novel step-by-step design methodology to tune prognostic algorithm hyper-parameters, which allows to guarantee that obtained results do not violate fundamental precision bounds. As an illustrative example, both the predictive BCRLB concept and the proposed design methodology are applied to the problem of End-of-Discharge (EoD) time prognostics in lithium-ion batteries.
abstract_unstemmed	System states are related, directly or indirectly, to health condition indicators. Indeed, critical system failures can be efficiently characterized through a state space manifold. This fact has encouraged the development of a series of failure prognostic frameworks based on Bayesian processors (e.g. particle or unscented Kalman filters), which efficiently help to estimate the Time-of-Failure (ToF) probability distribution in nonlinear, non- Gaussian, systems with uncertain future operating profiles. However, it is still unclear how to determine the efficacy of these methods, since the Prognostics and Health Management (PHM) community has not developed rigorous theoretical frameworks that could help to define proper performance indicators. In this regard, this article introduces novel prognostic performance metric based on the concept of Bayesian Cramér-Rao Lower Bounds (BCRLBs) for the predicted state mean square error (MSE), which is conditional to measurement data and model dynamics; providing a formal mathematical definition of the prognostic problem. Furthermore, we propose a novel step-by-step design methodology to tune prognostic algorithm hyper-parameters, which allows to guarantee that obtained results do not violate fundamental precision bounds. As an illustrative example, both the predictive BCRLB concept and the proposed design methodology are applied to the problem of End-of-Discharge (EoD) time prognostics in lithium-ion batteries.
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title_short	Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design
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up_date	2024-07-06T19:56:09.613Z
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Conditional predictive Bayesian Cramér-Rao Lower Bounds for prognostic algorithms design

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