Optimal task-driven time-dependent covariate-based maintenance policy

In this paper, a multi-component series system is considered. The system is monitored periodically. The exact cause of failure is assumed to be masked or missed. In the masked setup, the exact cause of failure is unknown but the set to which it belongs, called the masked set, is known. While in the...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Misaii, Hasan [verfasserIn] Fouladirad, Mitra Haghighi, Firoozeh

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Perfect corrective maintenance Minimal corrective maintenance Imperfect corrective maintenance Task-driven decision-making Masked data Statistical and machine learning algorithms

Übergeordnetes Werk:	Enthalten in: Journal of computational and applied mathematics - Amsterdam [u.a.] : North-Holland, 1975, 435
Übergeordnetes Werk:	volume:435

DOI / URN:	10.1016/j.cam.2023.115315

Katalog-ID:	ELV062149733

Internformat


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245	1	0	\|a Optimal task-driven time-dependent covariate-based maintenance policy
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520			\|a In this paper, a multi-component series system is considered. The system is monitored periodically. The exact cause of failure is assumed to be masked or missed. In the masked setup, the exact cause of failure is unknown but the set to which it belongs, called the masked set, is known. While in the missing setup, there is no information about the exact cause of failure. A time-dependent covariate-based maintenance policy is proposed such that the maintenance action and cost of the failed components at inspection times depend on several factors and can vary. The component lifetime distributions are considered unknown. The proposed maintenance policy is optimized using some task-driven decision-making statistical learning methods. Finally, the applicability of the proposed theory is analyzed through some numerical analysis. The results are compared to the case where lifetime distributions are known as a benchmark.
650		4	\|a Perfect corrective maintenance
650		4	\|a Minimal corrective maintenance
650		4	\|a Imperfect corrective maintenance
650		4	\|a Task-driven decision-making
650		4	\|a Masked data
650		4	\|a Statistical and machine learning algorithms
700	1		\|a Fouladirad, Mitra \|4 oth
700	1		\|a Haghighi, Firoozeh \|4 oth
773	0	8	\|i Enthalten in \|t Journal of computational and applied mathematics \|d Amsterdam [u.a.] : North-Holland, 1975 \|g 435 \|h Online-Ressource \|w (DE-627)266889204 \|w (DE-600)1468806-2 \|w (DE-576)075962373 \|7 nnns
773	1	8	\|g volume:435
912			\|a GBV_USEFLAG_U
912			\|a GBV_ELV
912			\|a SYSFLAG_U
912			\|a SSG-OLC-MAT
912			\|a SSG-OPC-MAT
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
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_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2009
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_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4012
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_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
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_4367
912			\|a GBV_ILN_4392
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 31.00 \|q VZ
951			\|a AR
952			\|d 435

Indexfelder

author_variant	h m hm
matchkey_str	misaiihasanfouladiradmitrahaghighifirooz:2023----:piatsdietmdpnetoaitbsd
hierarchy_sort_str	2023
bklnumber	31.00
publishDate	2023
allfields	10.1016/j.cam.2023.115315 doi (DE-627)ELV062149733 (ELSEVIER)S0377-0427(23)00259-5 DE-627 ger DE-627 rda eng 510 VZ 31.00 bkl Misaii, Hasan verfasserin aut Optimal task-driven time-dependent covariate-based maintenance policy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a multi-component series system is considered. The system is monitored periodically. The exact cause of failure is assumed to be masked or missed. In the masked setup, the exact cause of failure is unknown but the set to which it belongs, called the masked set, is known. While in the missing setup, there is no information about the exact cause of failure. A time-dependent covariate-based maintenance policy is proposed such that the maintenance action and cost of the failed components at inspection times depend on several factors and can vary. The component lifetime distributions are considered unknown. The proposed maintenance policy is optimized using some task-driven decision-making statistical learning methods. Finally, the applicability of the proposed theory is analyzed through some numerical analysis. The results are compared to the case where lifetime distributions are known as a benchmark. Perfect corrective maintenance Minimal corrective maintenance Imperfect corrective maintenance Task-driven decision-making Masked data Statistical and machine learning algorithms Fouladirad, Mitra oth Haghighi, Firoozeh oth Enthalten in Journal of computational and applied mathematics Amsterdam [u.a.] : North-Holland, 1975 435 Online-Ressource (DE-627)266889204 (DE-600)1468806-2 (DE-576)075962373 nnns volume:435 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-MAT SSG-OPC-MAT 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_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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 31.00 VZ AR 435
spelling	10.1016/j.cam.2023.115315 doi (DE-627)ELV062149733 (ELSEVIER)S0377-0427(23)00259-5 DE-627 ger DE-627 rda eng 510 VZ 31.00 bkl Misaii, Hasan verfasserin aut Optimal task-driven time-dependent covariate-based maintenance policy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a multi-component series system is considered. The system is monitored periodically. The exact cause of failure is assumed to be masked or missed. In the masked setup, the exact cause of failure is unknown but the set to which it belongs, called the masked set, is known. While in the missing setup, there is no information about the exact cause of failure. A time-dependent covariate-based maintenance policy is proposed such that the maintenance action and cost of the failed components at inspection times depend on several factors and can vary. The component lifetime distributions are considered unknown. The proposed maintenance policy is optimized using some task-driven decision-making statistical learning methods. Finally, the applicability of the proposed theory is analyzed through some numerical analysis. The results are compared to the case where lifetime distributions are known as a benchmark. Perfect corrective maintenance Minimal corrective maintenance Imperfect corrective maintenance Task-driven decision-making Masked data Statistical and machine learning algorithms Fouladirad, Mitra oth Haghighi, Firoozeh oth Enthalten in Journal of computational and applied mathematics Amsterdam [u.a.] : North-Holland, 1975 435 Online-Ressource (DE-627)266889204 (DE-600)1468806-2 (DE-576)075962373 nnns volume:435 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-MAT SSG-OPC-MAT 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_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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 31.00 VZ AR 435
allfields_unstemmed	10.1016/j.cam.2023.115315 doi (DE-627)ELV062149733 (ELSEVIER)S0377-0427(23)00259-5 DE-627 ger DE-627 rda eng 510 VZ 31.00 bkl Misaii, Hasan verfasserin aut Optimal task-driven time-dependent covariate-based maintenance policy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a multi-component series system is considered. The system is monitored periodically. The exact cause of failure is assumed to be masked or missed. In the masked setup, the exact cause of failure is unknown but the set to which it belongs, called the masked set, is known. While in the missing setup, there is no information about the exact cause of failure. A time-dependent covariate-based maintenance policy is proposed such that the maintenance action and cost of the failed components at inspection times depend on several factors and can vary. The component lifetime distributions are considered unknown. The proposed maintenance policy is optimized using some task-driven decision-making statistical learning methods. Finally, the applicability of the proposed theory is analyzed through some numerical analysis. The results are compared to the case where lifetime distributions are known as a benchmark. Perfect corrective maintenance Minimal corrective maintenance Imperfect corrective maintenance Task-driven decision-making Masked data Statistical and machine learning algorithms Fouladirad, Mitra oth Haghighi, Firoozeh oth Enthalten in Journal of computational and applied mathematics Amsterdam [u.a.] : North-Holland, 1975 435 Online-Ressource (DE-627)266889204 (DE-600)1468806-2 (DE-576)075962373 nnns volume:435 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-MAT SSG-OPC-MAT 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_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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 31.00 VZ AR 435
allfieldsGer	10.1016/j.cam.2023.115315 doi (DE-627)ELV062149733 (ELSEVIER)S0377-0427(23)00259-5 DE-627 ger DE-627 rda eng 510 VZ 31.00 bkl Misaii, Hasan verfasserin aut Optimal task-driven time-dependent covariate-based maintenance policy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a multi-component series system is considered. The system is monitored periodically. The exact cause of failure is assumed to be masked or missed. In the masked setup, the exact cause of failure is unknown but the set to which it belongs, called the masked set, is known. While in the missing setup, there is no information about the exact cause of failure. A time-dependent covariate-based maintenance policy is proposed such that the maintenance action and cost of the failed components at inspection times depend on several factors and can vary. The component lifetime distributions are considered unknown. The proposed maintenance policy is optimized using some task-driven decision-making statistical learning methods. Finally, the applicability of the proposed theory is analyzed through some numerical analysis. The results are compared to the case where lifetime distributions are known as a benchmark. Perfect corrective maintenance Minimal corrective maintenance Imperfect corrective maintenance Task-driven decision-making Masked data Statistical and machine learning algorithms Fouladirad, Mitra oth Haghighi, Firoozeh oth Enthalten in Journal of computational and applied mathematics Amsterdam [u.a.] : North-Holland, 1975 435 Online-Ressource (DE-627)266889204 (DE-600)1468806-2 (DE-576)075962373 nnns volume:435 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-MAT SSG-OPC-MAT 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_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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 31.00 VZ AR 435
allfieldsSound	10.1016/j.cam.2023.115315 doi (DE-627)ELV062149733 (ELSEVIER)S0377-0427(23)00259-5 DE-627 ger DE-627 rda eng 510 VZ 31.00 bkl Misaii, Hasan verfasserin aut Optimal task-driven time-dependent covariate-based maintenance policy 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a multi-component series system is considered. The system is monitored periodically. The exact cause of failure is assumed to be masked or missed. In the masked setup, the exact cause of failure is unknown but the set to which it belongs, called the masked set, is known. While in the missing setup, there is no information about the exact cause of failure. A time-dependent covariate-based maintenance policy is proposed such that the maintenance action and cost of the failed components at inspection times depend on several factors and can vary. The component lifetime distributions are considered unknown. The proposed maintenance policy is optimized using some task-driven decision-making statistical learning methods. Finally, the applicability of the proposed theory is analyzed through some numerical analysis. The results are compared to the case where lifetime distributions are known as a benchmark. Perfect corrective maintenance Minimal corrective maintenance Imperfect corrective maintenance Task-driven decision-making Masked data Statistical and machine learning algorithms Fouladirad, Mitra oth Haghighi, Firoozeh oth Enthalten in Journal of computational and applied mathematics Amsterdam [u.a.] : North-Holland, 1975 435 Online-Ressource (DE-627)266889204 (DE-600)1468806-2 (DE-576)075962373 nnns volume:435 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-MAT SSG-OPC-MAT 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_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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 31.00 VZ AR 435
language	English
source	Enthalten in Journal of computational and applied mathematics 435 volume:435
sourceStr	Enthalten in Journal of computational and applied mathematics 435 volume:435
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Perfect corrective maintenance Minimal corrective maintenance Imperfect corrective maintenance Task-driven decision-making Masked data Statistical and machine learning algorithms
dewey-raw	510
isfreeaccess_bool	false
container_title	Journal of computational and applied mathematics
authorswithroles_txt_mv	Misaii, Hasan @@aut@@ Fouladirad, Mitra @@oth@@ Haghighi, Firoozeh @@oth@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	266889204
dewey-sort	3510
id	ELV062149733
language_de	englisch
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author	Misaii, Hasan
spellingShingle	Misaii, Hasan ddc 510 bkl 31.00 misc Perfect corrective maintenance misc Minimal corrective maintenance misc Imperfect corrective maintenance misc Task-driven decision-making misc Masked data misc Statistical and machine learning algorithms Optimal task-driven time-dependent covariate-based maintenance policy
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topic_title	510 VZ 31.00 bkl Optimal task-driven time-dependent covariate-based maintenance policy Perfect corrective maintenance Minimal corrective maintenance Imperfect corrective maintenance Task-driven decision-making Masked data Statistical and machine learning algorithms
topic	ddc 510 bkl 31.00 misc Perfect corrective maintenance misc Minimal corrective maintenance misc Imperfect corrective maintenance misc Task-driven decision-making misc Masked data misc Statistical and machine learning algorithms
topic_unstemmed	ddc 510 bkl 31.00 misc Perfect corrective maintenance misc Minimal corrective maintenance misc Imperfect corrective maintenance misc Task-driven decision-making misc Masked data misc Statistical and machine learning algorithms
topic_browse	ddc 510 bkl 31.00 misc Perfect corrective maintenance misc Minimal corrective maintenance misc Imperfect corrective maintenance misc Task-driven decision-making misc Masked data misc Statistical and machine learning algorithms
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title	Optimal task-driven time-dependent covariate-based maintenance policy
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title_full	Optimal task-driven time-dependent covariate-based maintenance policy
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title_sort	optimal task-driven time-dependent covariate-based maintenance policy
title_auth	Optimal task-driven time-dependent covariate-based maintenance policy
abstract	In this paper, a multi-component series system is considered. The system is monitored periodically. The exact cause of failure is assumed to be masked or missed. In the masked setup, the exact cause of failure is unknown but the set to which it belongs, called the masked set, is known. While in the missing setup, there is no information about the exact cause of failure. A time-dependent covariate-based maintenance policy is proposed such that the maintenance action and cost of the failed components at inspection times depend on several factors and can vary. The component lifetime distributions are considered unknown. The proposed maintenance policy is optimized using some task-driven decision-making statistical learning methods. Finally, the applicability of the proposed theory is analyzed through some numerical analysis. The results are compared to the case where lifetime distributions are known as a benchmark.
abstractGer	In this paper, a multi-component series system is considered. The system is monitored periodically. The exact cause of failure is assumed to be masked or missed. In the masked setup, the exact cause of failure is unknown but the set to which it belongs, called the masked set, is known. While in the missing setup, there is no information about the exact cause of failure. A time-dependent covariate-based maintenance policy is proposed such that the maintenance action and cost of the failed components at inspection times depend on several factors and can vary. The component lifetime distributions are considered unknown. The proposed maintenance policy is optimized using some task-driven decision-making statistical learning methods. Finally, the applicability of the proposed theory is analyzed through some numerical analysis. The results are compared to the case where lifetime distributions are known as a benchmark.
abstract_unstemmed	In this paper, a multi-component series system is considered. The system is monitored periodically. The exact cause of failure is assumed to be masked or missed. In the masked setup, the exact cause of failure is unknown but the set to which it belongs, called the masked set, is known. While in the missing setup, there is no information about the exact cause of failure. A time-dependent covariate-based maintenance policy is proposed such that the maintenance action and cost of the failed components at inspection times depend on several factors and can vary. The component lifetime distributions are considered unknown. The proposed maintenance policy is optimized using some task-driven decision-making statistical learning methods. Finally, the applicability of the proposed theory is analyzed through some numerical analysis. The results are compared to the case where lifetime distributions are known as a benchmark.
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title_short	Optimal task-driven time-dependent covariate-based maintenance policy
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author2	Fouladirad, Mitra Haghighi, Firoozeh
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doi_str	10.1016/j.cam.2023.115315
up_date	2024-07-06T18:27:45.917Z
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