A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms

Failure prognostic algorithms require to reduce the computational burden associated with their implementation to ensure real-time performance in embedded systems. In this regard, this paper presents a method that allows to significantly reduce this computational cost in the case of particle-filter-b...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Rozas, Heraldo [verfasserIn] Jaramillo, Francisco [verfasserIn] Perez, Aramis [verfasserIn] Jimenez, Diego [verfasserIn] Orchard, Marcos E. [verfasserIn] Medjaher, Kamal [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Prognostic algorithms Time-of-Failure probability distribution Online performance assessment

Übergeordnetes Werk:	Enthalten in: Mechanical systems and signal processing - Amsterdam [u.a.] : Elsevier, 1987, 135
Übergeordnetes Werk:	volume:135

DOI / URN:	10.1016/j.ymssp.2019.106421

Katalog-ID:	ELV003102580

Internformat


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520			\|a Failure prognostic algorithms require to reduce the computational burden associated with their implementation to ensure real-time performance in embedded systems. In this regard, this paper presents a method that allows to significantly reduce this computational cost in the case of particle-filter-based prognostic algorithms, which is based on a time-variant prognostic update rate. In this proposed scheme, the performance of the prognostic algorithm within short-term prediction horizons is continuously compared with respect to the outcome of Bayesian state estimators. Only if the discrepancy between prior and posterior knowledge is greater than a given threshold, it is suggested to execute the prognostic algorithm once again and update Time-of-Failure estimates. In addition, a novel metric to evaluate the performance of any prognostic algorithm in real-time is hereby presented. The proposed actualization scheme is implemented, tested, and validated in two case studies related to the problem of State-of-Charge (SOC) prognostics. The obtained results show that the proposed strategy allows to significantly reduce the computational cost while keeping the standards in terms of algorithm efficacy.
650		4	\|a Prognostic algorithms
650		4	\|a Time-of-Failure probability distribution
650		4	\|a Online performance assessment
700	1		\|a Jaramillo, Francisco \|e verfasserin \|4 aut
700	1		\|a Perez, Aramis \|e verfasserin \|0 (orcid)0000-0002-6783-2659 \|4 aut
700	1		\|a Jimenez, Diego \|e verfasserin \|4 aut
700	1		\|a Orchard, Marcos E. \|e verfasserin \|4 aut
700	1		\|a Medjaher, Kamal \|e verfasserin \|0 (orcid)0000-0001-7895-5569 \|4 aut
773	0	8	\|i Enthalten in \|t Mechanical systems and signal processing \|d Amsterdam [u.a.] : Elsevier, 1987 \|g 135 \|h Online-Ressource \|w (DE-627)267838670 \|w (DE-600)1471003-1 \|w (DE-576)253127629 \|x 1096-1216 \|7 nnns
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912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 50.32 \|j Dynamik \|j Schwingungslehre \|x Technische Mechanik
936	b	k	\|a 50.16 \|j Technische Zuverlässigkeit \|j Instandhaltung
951			\|a AR
952			\|d 135

Indexfelder

author_variant	h r hr f j fj a p ap d j dj m e o me meo k m km
matchkey_str	article:10961216:2019----::mtofrhrdcinfhcmuainlotsoitdihhipeettooprilfle
hierarchy_sort_str	2019
bklnumber	50.32 50.16
publishDate	2019
allfields	10.1016/j.ymssp.2019.106421 doi (DE-627)ELV003102580 (ELSEVIER)S0888-3270(19)30642-9 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Rozas, Heraldo verfasserin aut A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Failure prognostic algorithms require to reduce the computational burden associated with their implementation to ensure real-time performance in embedded systems. In this regard, this paper presents a method that allows to significantly reduce this computational cost in the case of particle-filter-based prognostic algorithms, which is based on a time-variant prognostic update rate. In this proposed scheme, the performance of the prognostic algorithm within short-term prediction horizons is continuously compared with respect to the outcome of Bayesian state estimators. Only if the discrepancy between prior and posterior knowledge is greater than a given threshold, it is suggested to execute the prognostic algorithm once again and update Time-of-Failure estimates. In addition, a novel metric to evaluate the performance of any prognostic algorithm in real-time is hereby presented. The proposed actualization scheme is implemented, tested, and validated in two case studies related to the problem of State-of-Charge (SOC) prognostics. The obtained results show that the proposed strategy allows to significantly reduce the computational cost while keeping the standards in terms of algorithm efficacy. Prognostic algorithms Time-of-Failure probability distribution Online performance assessment Jaramillo, Francisco verfasserin aut Perez, Aramis verfasserin (orcid)0000-0002-6783-2659 aut Jimenez, Diego verfasserin aut Orchard, Marcos E. verfasserin aut Medjaher, Kamal verfasserin (orcid)0000-0001-7895-5569 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 135 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:135 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_2010 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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 135
spelling	10.1016/j.ymssp.2019.106421 doi (DE-627)ELV003102580 (ELSEVIER)S0888-3270(19)30642-9 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Rozas, Heraldo verfasserin aut A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Failure prognostic algorithms require to reduce the computational burden associated with their implementation to ensure real-time performance in embedded systems. In this regard, this paper presents a method that allows to significantly reduce this computational cost in the case of particle-filter-based prognostic algorithms, which is based on a time-variant prognostic update rate. In this proposed scheme, the performance of the prognostic algorithm within short-term prediction horizons is continuously compared with respect to the outcome of Bayesian state estimators. Only if the discrepancy between prior and posterior knowledge is greater than a given threshold, it is suggested to execute the prognostic algorithm once again and update Time-of-Failure estimates. In addition, a novel metric to evaluate the performance of any prognostic algorithm in real-time is hereby presented. The proposed actualization scheme is implemented, tested, and validated in two case studies related to the problem of State-of-Charge (SOC) prognostics. The obtained results show that the proposed strategy allows to significantly reduce the computational cost while keeping the standards in terms of algorithm efficacy. Prognostic algorithms Time-of-Failure probability distribution Online performance assessment Jaramillo, Francisco verfasserin aut Perez, Aramis verfasserin (orcid)0000-0002-6783-2659 aut Jimenez, Diego verfasserin aut Orchard, Marcos E. verfasserin aut Medjaher, Kamal verfasserin (orcid)0000-0001-7895-5569 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 135 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:135 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_2010 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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 135
allfields_unstemmed	10.1016/j.ymssp.2019.106421 doi (DE-627)ELV003102580 (ELSEVIER)S0888-3270(19)30642-9 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Rozas, Heraldo verfasserin aut A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Failure prognostic algorithms require to reduce the computational burden associated with their implementation to ensure real-time performance in embedded systems. In this regard, this paper presents a method that allows to significantly reduce this computational cost in the case of particle-filter-based prognostic algorithms, which is based on a time-variant prognostic update rate. In this proposed scheme, the performance of the prognostic algorithm within short-term prediction horizons is continuously compared with respect to the outcome of Bayesian state estimators. Only if the discrepancy between prior and posterior knowledge is greater than a given threshold, it is suggested to execute the prognostic algorithm once again and update Time-of-Failure estimates. In addition, a novel metric to evaluate the performance of any prognostic algorithm in real-time is hereby presented. The proposed actualization scheme is implemented, tested, and validated in two case studies related to the problem of State-of-Charge (SOC) prognostics. The obtained results show that the proposed strategy allows to significantly reduce the computational cost while keeping the standards in terms of algorithm efficacy. Prognostic algorithms Time-of-Failure probability distribution Online performance assessment Jaramillo, Francisco verfasserin aut Perez, Aramis verfasserin (orcid)0000-0002-6783-2659 aut Jimenez, Diego verfasserin aut Orchard, Marcos E. verfasserin aut Medjaher, Kamal verfasserin (orcid)0000-0001-7895-5569 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 135 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:135 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_2010 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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 135
allfieldsGer	10.1016/j.ymssp.2019.106421 doi (DE-627)ELV003102580 (ELSEVIER)S0888-3270(19)30642-9 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Rozas, Heraldo verfasserin aut A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Failure prognostic algorithms require to reduce the computational burden associated with their implementation to ensure real-time performance in embedded systems. In this regard, this paper presents a method that allows to significantly reduce this computational cost in the case of particle-filter-based prognostic algorithms, which is based on a time-variant prognostic update rate. In this proposed scheme, the performance of the prognostic algorithm within short-term prediction horizons is continuously compared with respect to the outcome of Bayesian state estimators. Only if the discrepancy between prior and posterior knowledge is greater than a given threshold, it is suggested to execute the prognostic algorithm once again and update Time-of-Failure estimates. In addition, a novel metric to evaluate the performance of any prognostic algorithm in real-time is hereby presented. The proposed actualization scheme is implemented, tested, and validated in two case studies related to the problem of State-of-Charge (SOC) prognostics. The obtained results show that the proposed strategy allows to significantly reduce the computational cost while keeping the standards in terms of algorithm efficacy. Prognostic algorithms Time-of-Failure probability distribution Online performance assessment Jaramillo, Francisco verfasserin aut Perez, Aramis verfasserin (orcid)0000-0002-6783-2659 aut Jimenez, Diego verfasserin aut Orchard, Marcos E. verfasserin aut Medjaher, Kamal verfasserin (orcid)0000-0001-7895-5569 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 135 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:135 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_2010 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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 135
allfieldsSound	10.1016/j.ymssp.2019.106421 doi (DE-627)ELV003102580 (ELSEVIER)S0888-3270(19)30642-9 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Rozas, Heraldo verfasserin aut A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Failure prognostic algorithms require to reduce the computational burden associated with their implementation to ensure real-time performance in embedded systems. In this regard, this paper presents a method that allows to significantly reduce this computational cost in the case of particle-filter-based prognostic algorithms, which is based on a time-variant prognostic update rate. In this proposed scheme, the performance of the prognostic algorithm within short-term prediction horizons is continuously compared with respect to the outcome of Bayesian state estimators. Only if the discrepancy between prior and posterior knowledge is greater than a given threshold, it is suggested to execute the prognostic algorithm once again and update Time-of-Failure estimates. In addition, a novel metric to evaluate the performance of any prognostic algorithm in real-time is hereby presented. The proposed actualization scheme is implemented, tested, and validated in two case studies related to the problem of State-of-Charge (SOC) prognostics. The obtained results show that the proposed strategy allows to significantly reduce the computational cost while keeping the standards in terms of algorithm efficacy. Prognostic algorithms Time-of-Failure probability distribution Online performance assessment Jaramillo, Francisco verfasserin aut Perez, Aramis verfasserin (orcid)0000-0002-6783-2659 aut Jimenez, Diego verfasserin aut Orchard, Marcos E. verfasserin aut Medjaher, Kamal verfasserin (orcid)0000-0001-7895-5569 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 135 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:135 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_2010 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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 135
language	English
source	Enthalten in Mechanical systems and signal processing 135 volume:135
sourceStr	Enthalten in Mechanical systems and signal processing 135 volume:135
format_phy_str_mv	Article
bklname	Dynamik Schwingungslehre Technische Zuverlässigkeit Instandhaltung
institution	findex.gbv.de
topic_facet	Prognostic algorithms Time-of-Failure probability distribution Online performance assessment
dewey-raw	004
isfreeaccess_bool	false
container_title	Mechanical systems and signal processing
authorswithroles_txt_mv	Rozas, Heraldo @@aut@@ Jaramillo, Francisco @@aut@@ Perez, Aramis @@aut@@ Jimenez, Diego @@aut@@ Orchard, Marcos E. @@aut@@ Medjaher, Kamal @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	267838670
dewey-sort	14
id	ELV003102580
language_de	englisch
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author	Rozas, Heraldo
spellingShingle	Rozas, Heraldo ddc 004 bkl 50.32 bkl 50.16 misc Prognostic algorithms misc Time-of-Failure probability distribution misc Online performance assessment A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms
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topic_title	004 DE-600 50.32 bkl 50.16 bkl A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms Prognostic algorithms Time-of-Failure probability distribution Online performance assessment
topic	ddc 004 bkl 50.32 bkl 50.16 misc Prognostic algorithms misc Time-of-Failure probability distribution misc Online performance assessment
topic_unstemmed	ddc 004 bkl 50.32 bkl 50.16 misc Prognostic algorithms misc Time-of-Failure probability distribution misc Online performance assessment
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title	A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms
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title_full	A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms
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author_browse	Rozas, Heraldo Jaramillo, Francisco Perez, Aramis Jimenez, Diego Orchard, Marcos E. Medjaher, Kamal
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title_sort	a method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms
title_auth	A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms
abstract	Failure prognostic algorithms require to reduce the computational burden associated with their implementation to ensure real-time performance in embedded systems. In this regard, this paper presents a method that allows to significantly reduce this computational cost in the case of particle-filter-based prognostic algorithms, which is based on a time-variant prognostic update rate. In this proposed scheme, the performance of the prognostic algorithm within short-term prediction horizons is continuously compared with respect to the outcome of Bayesian state estimators. Only if the discrepancy between prior and posterior knowledge is greater than a given threshold, it is suggested to execute the prognostic algorithm once again and update Time-of-Failure estimates. In addition, a novel metric to evaluate the performance of any prognostic algorithm in real-time is hereby presented. The proposed actualization scheme is implemented, tested, and validated in two case studies related to the problem of State-of-Charge (SOC) prognostics. The obtained results show that the proposed strategy allows to significantly reduce the computational cost while keeping the standards in terms of algorithm efficacy.
abstractGer	Failure prognostic algorithms require to reduce the computational burden associated with their implementation to ensure real-time performance in embedded systems. In this regard, this paper presents a method that allows to significantly reduce this computational cost in the case of particle-filter-based prognostic algorithms, which is based on a time-variant prognostic update rate. In this proposed scheme, the performance of the prognostic algorithm within short-term prediction horizons is continuously compared with respect to the outcome of Bayesian state estimators. Only if the discrepancy between prior and posterior knowledge is greater than a given threshold, it is suggested to execute the prognostic algorithm once again and update Time-of-Failure estimates. In addition, a novel metric to evaluate the performance of any prognostic algorithm in real-time is hereby presented. The proposed actualization scheme is implemented, tested, and validated in two case studies related to the problem of State-of-Charge (SOC) prognostics. The obtained results show that the proposed strategy allows to significantly reduce the computational cost while keeping the standards in terms of algorithm efficacy.
abstract_unstemmed	Failure prognostic algorithms require to reduce the computational burden associated with their implementation to ensure real-time performance in embedded systems. In this regard, this paper presents a method that allows to significantly reduce this computational cost in the case of particle-filter-based prognostic algorithms, which is based on a time-variant prognostic update rate. In this proposed scheme, the performance of the prognostic algorithm within short-term prediction horizons is continuously compared with respect to the outcome of Bayesian state estimators. Only if the discrepancy between prior and posterior knowledge is greater than a given threshold, it is suggested to execute the prognostic algorithm once again and update Time-of-Failure estimates. In addition, a novel metric to evaluate the performance of any prognostic algorithm in real-time is hereby presented. The proposed actualization scheme is implemented, tested, and validated in two case studies related to the problem of State-of-Charge (SOC) prognostics. The obtained results show that the proposed strategy allows to significantly reduce the computational cost while keeping the standards in terms of algorithm efficacy.
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title_short	A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms
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author2	Jaramillo, Francisco Perez, Aramis Jimenez, Diego Orchard, Marcos E. Medjaher, Kamal
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up_date	2024-07-06T18:29:56.421Z
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A method for the reduction of the computational cost associated with the implementation of particle-filter-based failure prognostic algorithms

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