Research of acquisition and prediction method in early weak information of locomotive traction system

Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Bin Ren [verfasserIn] Siwen Li [verfasserIn] Shaopu Yang [verfasserIn] Wentao Song [verfasserIn] Yonggang Jiao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	locomotives feature extraction fault diagnosis gears different time scale fault features uncertain factors dynamic cascade empirical mode decomposition characteristic frequency acquisition inhibition mode mixing locomotive gear early period fault deterioration weak failures crash economic loss failure period mechanical equipment locomotive traction system early weak information prediction method early fault prognosis weak faults envelope solution method optimal intrinsic mode function acquisition

Übergeordnetes Werk:	In: The Journal of Engineering - Wiley, 2013, (2019)
Übergeordnetes Werk:	year:2019

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1049/joe.2018.9058

Katalog-ID:	DOAJ049915126

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ049915126
003	DE-627
005	20230308145848.0
007	cr uuu---uuuuu
008	230227s2019 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1049/joe.2018.9058 \|2 doi
035			\|a (DE-627)DOAJ049915126
035			\|a (DE-599)DOAJ328a527d155046ba95ee2ccc4e25fccc
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TA1-2040
100	0		\|a Bin Ren \|e verfasserin \|4 aut
245	1	0	\|a Research of acquisition and prediction method in early weak information of locomotive traction system
264		1	\|c 2019
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure. It would be of benefit to prevent the fault deterioration if faults could be identified in early period, with the states of key parts obtained. Locomotive gear was chosen as the research object here, a method of inhibition mode mixing and characteristic frequency acquisition based on dynamic cascade empirical mode decomposition is put forward for the weakness and aliasing out of uncertain factors. The fault features are much more obviously presented in different time scale by optimal intrinsic mode function (IMF) acquisition and the improvement of envelope solution method. The results of experiment show that weak faults can be obtained under much noise, and an effective method is provided for early fault prognosis and diagnosis.
650		4	\|a locomotives
650		4	\|a feature extraction
650		4	\|a fault diagnosis
650		4	\|a gears
650		4	\|a different time scale
650		4	\|a fault features
650		4	\|a uncertain factors
650		4	\|a dynamic cascade empirical mode decomposition
650		4	\|a characteristic frequency acquisition
650		4	\|a inhibition mode mixing
650		4	\|a locomotive gear
650		4	\|a early period
650		4	\|a fault deterioration
650		4	\|a weak failures
650		4	\|a crash
650		4	\|a economic loss
650		4	\|a failure period
650		4	\|a mechanical equipment
650		4	\|a locomotive traction system
650		4	\|a early weak information
650		4	\|a prediction method
650		4	\|a early fault prognosis
650		4	\|a weak faults
650		4	\|a envelope solution method
650		4	\|a optimal intrinsic mode function acquisition
653		0	\|a Engineering (General). Civil engineering (General)
700	0		\|a Siwen Li \|e verfasserin \|4 aut
700	0		\|a Shaopu Yang \|e verfasserin \|4 aut
700	0		\|a Wentao Song \|e verfasserin \|4 aut
700	0		\|a Yonggang Jiao \|e verfasserin \|4 aut
773	0	8	\|i In \|t The Journal of Engineering \|d Wiley, 2013 \|g (2019) \|w (DE-627)75682270X \|w (DE-600)2727074-9 \|x 20513305 \|7 nnns
773	1	8	\|g year:2019
856	4	0	\|u https://doi.org/10.1049/joe.2018.9058 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/328a527d155046ba95ee2ccc4e25fccc \|z kostenfrei
856	4	0	\|u https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9058 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2051-3305 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
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_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
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_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
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_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
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_4336
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|j 2019

Indexfelder

author_variant	b r br s l sl s y sy w s ws y j yj
matchkey_str	article:20513305:2019----::eerhfcustoadrdcinehdnalwaifrainfo
hierarchy_sort_str	2019
callnumber-subject-code	TA
publishDate	2019
allfields	10.1049/joe.2018.9058 doi (DE-627)DOAJ049915126 (DE-599)DOAJ328a527d155046ba95ee2ccc4e25fccc DE-627 ger DE-627 rakwb eng TA1-2040 Bin Ren verfasserin aut Research of acquisition and prediction method in early weak information of locomotive traction system 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure. It would be of benefit to prevent the fault deterioration if faults could be identified in early period, with the states of key parts obtained. Locomotive gear was chosen as the research object here, a method of inhibition mode mixing and characteristic frequency acquisition based on dynamic cascade empirical mode decomposition is put forward for the weakness and aliasing out of uncertain factors. The fault features are much more obviously presented in different time scale by optimal intrinsic mode function (IMF) acquisition and the improvement of envelope solution method. The results of experiment show that weak faults can be obtained under much noise, and an effective method is provided for early fault prognosis and diagnosis. locomotives feature extraction fault diagnosis gears different time scale fault features uncertain factors dynamic cascade empirical mode decomposition characteristic frequency acquisition inhibition mode mixing locomotive gear early period fault deterioration weak failures crash economic loss failure period mechanical equipment locomotive traction system early weak information prediction method early fault prognosis weak faults envelope solution method optimal intrinsic mode function acquisition Engineering (General). Civil engineering (General) Siwen Li verfasserin aut Shaopu Yang verfasserin aut Wentao Song verfasserin aut Yonggang Jiao verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2018.9058 kostenfrei https://doaj.org/article/328a527d155046ba95ee2ccc4e25fccc kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9058 kostenfrei https://doaj.org/toc/2051-3305 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
spelling	10.1049/joe.2018.9058 doi (DE-627)DOAJ049915126 (DE-599)DOAJ328a527d155046ba95ee2ccc4e25fccc DE-627 ger DE-627 rakwb eng TA1-2040 Bin Ren verfasserin aut Research of acquisition and prediction method in early weak information of locomotive traction system 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure. It would be of benefit to prevent the fault deterioration if faults could be identified in early period, with the states of key parts obtained. Locomotive gear was chosen as the research object here, a method of inhibition mode mixing and characteristic frequency acquisition based on dynamic cascade empirical mode decomposition is put forward for the weakness and aliasing out of uncertain factors. The fault features are much more obviously presented in different time scale by optimal intrinsic mode function (IMF) acquisition and the improvement of envelope solution method. The results of experiment show that weak faults can be obtained under much noise, and an effective method is provided for early fault prognosis and diagnosis. locomotives feature extraction fault diagnosis gears different time scale fault features uncertain factors dynamic cascade empirical mode decomposition characteristic frequency acquisition inhibition mode mixing locomotive gear early period fault deterioration weak failures crash economic loss failure period mechanical equipment locomotive traction system early weak information prediction method early fault prognosis weak faults envelope solution method optimal intrinsic mode function acquisition Engineering (General). Civil engineering (General) Siwen Li verfasserin aut Shaopu Yang verfasserin aut Wentao Song verfasserin aut Yonggang Jiao verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2018.9058 kostenfrei https://doaj.org/article/328a527d155046ba95ee2ccc4e25fccc kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9058 kostenfrei https://doaj.org/toc/2051-3305 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfields_unstemmed	10.1049/joe.2018.9058 doi (DE-627)DOAJ049915126 (DE-599)DOAJ328a527d155046ba95ee2ccc4e25fccc DE-627 ger DE-627 rakwb eng TA1-2040 Bin Ren verfasserin aut Research of acquisition and prediction method in early weak information of locomotive traction system 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure. It would be of benefit to prevent the fault deterioration if faults could be identified in early period, with the states of key parts obtained. Locomotive gear was chosen as the research object here, a method of inhibition mode mixing and characteristic frequency acquisition based on dynamic cascade empirical mode decomposition is put forward for the weakness and aliasing out of uncertain factors. The fault features are much more obviously presented in different time scale by optimal intrinsic mode function (IMF) acquisition and the improvement of envelope solution method. The results of experiment show that weak faults can be obtained under much noise, and an effective method is provided for early fault prognosis and diagnosis. locomotives feature extraction fault diagnosis gears different time scale fault features uncertain factors dynamic cascade empirical mode decomposition characteristic frequency acquisition inhibition mode mixing locomotive gear early period fault deterioration weak failures crash economic loss failure period mechanical equipment locomotive traction system early weak information prediction method early fault prognosis weak faults envelope solution method optimal intrinsic mode function acquisition Engineering (General). Civil engineering (General) Siwen Li verfasserin aut Shaopu Yang verfasserin aut Wentao Song verfasserin aut Yonggang Jiao verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2018.9058 kostenfrei https://doaj.org/article/328a527d155046ba95ee2ccc4e25fccc kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9058 kostenfrei https://doaj.org/toc/2051-3305 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfieldsGer	10.1049/joe.2018.9058 doi (DE-627)DOAJ049915126 (DE-599)DOAJ328a527d155046ba95ee2ccc4e25fccc DE-627 ger DE-627 rakwb eng TA1-2040 Bin Ren verfasserin aut Research of acquisition and prediction method in early weak information of locomotive traction system 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure. It would be of benefit to prevent the fault deterioration if faults could be identified in early period, with the states of key parts obtained. Locomotive gear was chosen as the research object here, a method of inhibition mode mixing and characteristic frequency acquisition based on dynamic cascade empirical mode decomposition is put forward for the weakness and aliasing out of uncertain factors. The fault features are much more obviously presented in different time scale by optimal intrinsic mode function (IMF) acquisition and the improvement of envelope solution method. The results of experiment show that weak faults can be obtained under much noise, and an effective method is provided for early fault prognosis and diagnosis. locomotives feature extraction fault diagnosis gears different time scale fault features uncertain factors dynamic cascade empirical mode decomposition characteristic frequency acquisition inhibition mode mixing locomotive gear early period fault deterioration weak failures crash economic loss failure period mechanical equipment locomotive traction system early weak information prediction method early fault prognosis weak faults envelope solution method optimal intrinsic mode function acquisition Engineering (General). Civil engineering (General) Siwen Li verfasserin aut Shaopu Yang verfasserin aut Wentao Song verfasserin aut Yonggang Jiao verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2018.9058 kostenfrei https://doaj.org/article/328a527d155046ba95ee2ccc4e25fccc kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9058 kostenfrei https://doaj.org/toc/2051-3305 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfieldsSound	10.1049/joe.2018.9058 doi (DE-627)DOAJ049915126 (DE-599)DOAJ328a527d155046ba95ee2ccc4e25fccc DE-627 ger DE-627 rakwb eng TA1-2040 Bin Ren verfasserin aut Research of acquisition and prediction method in early weak information of locomotive traction system 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure. It would be of benefit to prevent the fault deterioration if faults could be identified in early period, with the states of key parts obtained. Locomotive gear was chosen as the research object here, a method of inhibition mode mixing and characteristic frequency acquisition based on dynamic cascade empirical mode decomposition is put forward for the weakness and aliasing out of uncertain factors. The fault features are much more obviously presented in different time scale by optimal intrinsic mode function (IMF) acquisition and the improvement of envelope solution method. The results of experiment show that weak faults can be obtained under much noise, and an effective method is provided for early fault prognosis and diagnosis. locomotives feature extraction fault diagnosis gears different time scale fault features uncertain factors dynamic cascade empirical mode decomposition characteristic frequency acquisition inhibition mode mixing locomotive gear early period fault deterioration weak failures crash economic loss failure period mechanical equipment locomotive traction system early weak information prediction method early fault prognosis weak faults envelope solution method optimal intrinsic mode function acquisition Engineering (General). Civil engineering (General) Siwen Li verfasserin aut Shaopu Yang verfasserin aut Wentao Song verfasserin aut Yonggang Jiao verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2018.9058 kostenfrei https://doaj.org/article/328a527d155046ba95ee2ccc4e25fccc kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9058 kostenfrei https://doaj.org/toc/2051-3305 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
language	English
source	In The Journal of Engineering (2019) year:2019
sourceStr	In The Journal of Engineering (2019) year:2019
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	locomotives feature extraction fault diagnosis gears different time scale fault features uncertain factors dynamic cascade empirical mode decomposition characteristic frequency acquisition inhibition mode mixing locomotive gear early period fault deterioration weak failures crash economic loss failure period mechanical equipment locomotive traction system early weak information prediction method early fault prognosis weak faults envelope solution method optimal intrinsic mode function acquisition Engineering (General). Civil engineering (General)
isfreeaccess_bool	true
container_title	The Journal of Engineering
authorswithroles_txt_mv	Bin Ren @@aut@@ Siwen Li @@aut@@ Shaopu Yang @@aut@@ Wentao Song @@aut@@ Yonggang Jiao @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	75682270X
id	DOAJ049915126
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ049915126</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308145848.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1049/joe.2018.9058</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ049915126</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ328a527d155046ba95ee2ccc4e25fccc</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA1-2040</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Bin Ren</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Research of acquisition and prediction method in early weak information of locomotive traction system</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure. It would be of benefit to prevent the fault deterioration if faults could be identified in early period, with the states of key parts obtained. Locomotive gear was chosen as the research object here, a method of inhibition mode mixing and characteristic frequency acquisition based on dynamic cascade empirical mode decomposition is put forward for the weakness and aliasing out of uncertain factors. The fault features are much more obviously presented in different time scale by optimal intrinsic mode function (IMF) acquisition and the improvement of envelope solution method. The results of experiment show that weak faults can be obtained under much noise, and an effective method is provided for early fault prognosis and diagnosis.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">locomotives</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">feature extraction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">fault diagnosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">gears</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">different time scale</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">fault features</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">uncertain factors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">dynamic cascade empirical mode decomposition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">characteristic frequency acquisition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">inhibition mode mixing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">locomotive gear</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">early period</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">fault deterioration</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">weak failures</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">crash</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">economic loss</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">failure period</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">mechanical equipment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">locomotive traction system</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">early weak information</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prediction method</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">early fault prognosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">weak faults</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">envelope solution method</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">optimal intrinsic mode function acquisition</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering (General). Civil engineering (General)</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Siwen Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shaopu Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wentao Song</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yonggang Jiao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">The Journal of Engineering</subfield><subfield code="d">Wiley, 2013</subfield><subfield code="g">(2019)</subfield><subfield code="w">(DE-627)75682270X</subfield><subfield code="w">(DE-600)2727074-9</subfield><subfield code="x">20513305</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">year:2019</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1049/joe.2018.9058</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/328a527d155046ba95ee2ccc4e25fccc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9058</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2051-3305</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="j">2019</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Bin Ren
spellingShingle	Bin Ren misc TA1-2040 misc locomotives misc feature extraction misc fault diagnosis misc gears misc different time scale misc fault features misc uncertain factors misc dynamic cascade empirical mode decomposition misc characteristic frequency acquisition misc inhibition mode mixing misc locomotive gear misc early period misc fault deterioration misc weak failures misc crash misc economic loss misc failure period misc mechanical equipment misc locomotive traction system misc early weak information misc prediction method misc early fault prognosis misc weak faults misc envelope solution method misc optimal intrinsic mode function acquisition misc Engineering (General). Civil engineering (General) Research of acquisition and prediction method in early weak information of locomotive traction system
authorStr	Bin Ren
ppnlink_with_tag_str_mv	@@773@@(DE-627)75682270X
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	TA1-2040
illustrated	Not Illustrated
issn	20513305
topic_title	TA1-2040 Research of acquisition and prediction method in early weak information of locomotive traction system locomotives feature extraction fault diagnosis gears different time scale fault features uncertain factors dynamic cascade empirical mode decomposition characteristic frequency acquisition inhibition mode mixing locomotive gear early period fault deterioration weak failures crash economic loss failure period mechanical equipment locomotive traction system early weak information prediction method early fault prognosis weak faults envelope solution method optimal intrinsic mode function acquisition
topic	misc TA1-2040 misc locomotives misc feature extraction misc fault diagnosis misc gears misc different time scale misc fault features misc uncertain factors misc dynamic cascade empirical mode decomposition misc characteristic frequency acquisition misc inhibition mode mixing misc locomotive gear misc early period misc fault deterioration misc weak failures misc crash misc economic loss misc failure period misc mechanical equipment misc locomotive traction system misc early weak information misc prediction method misc early fault prognosis misc weak faults misc envelope solution method misc optimal intrinsic mode function acquisition misc Engineering (General). Civil engineering (General)
topic_unstemmed	misc TA1-2040 misc locomotives misc feature extraction misc fault diagnosis misc gears misc different time scale misc fault features misc uncertain factors misc dynamic cascade empirical mode decomposition misc characteristic frequency acquisition misc inhibition mode mixing misc locomotive gear misc early period misc fault deterioration misc weak failures misc crash misc economic loss misc failure period misc mechanical equipment misc locomotive traction system misc early weak information misc prediction method misc early fault prognosis misc weak faults misc envelope solution method misc optimal intrinsic mode function acquisition misc Engineering (General). Civil engineering (General)
topic_browse	misc TA1-2040 misc locomotives misc feature extraction misc fault diagnosis misc gears misc different time scale misc fault features misc uncertain factors misc dynamic cascade empirical mode decomposition misc characteristic frequency acquisition misc inhibition mode mixing misc locomotive gear misc early period misc fault deterioration misc weak failures misc crash misc economic loss misc failure period misc mechanical equipment misc locomotive traction system misc early weak information misc prediction method misc early fault prognosis misc weak faults misc envelope solution method misc optimal intrinsic mode function acquisition misc Engineering (General). Civil engineering (General)
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	The Journal of Engineering
hierarchy_parent_id	75682270X
hierarchy_top_title	The Journal of Engineering
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)75682270X (DE-600)2727074-9
title	Research of acquisition and prediction method in early weak information of locomotive traction system
ctrlnum	(DE-627)DOAJ049915126 (DE-599)DOAJ328a527d155046ba95ee2ccc4e25fccc
title_full	Research of acquisition and prediction method in early weak information of locomotive traction system
author_sort	Bin Ren
journal	The Journal of Engineering
journalStr	The Journal of Engineering
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2019
contenttype_str_mv	txt
author_browse	Bin Ren Siwen Li Shaopu Yang Wentao Song Yonggang Jiao
class	TA1-2040
format_se	Elektronische Aufsätze
author-letter	Bin Ren
doi_str_mv	10.1049/joe.2018.9058
author2-role	verfasserin
title_sort	research of acquisition and prediction method in early weak information of locomotive traction system
callnumber	TA1-2040
title_auth	Research of acquisition and prediction method in early weak information of locomotive traction system
abstract	Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure. It would be of benefit to prevent the fault deterioration if faults could be identified in early period, with the states of key parts obtained. Locomotive gear was chosen as the research object here, a method of inhibition mode mixing and characteristic frequency acquisition based on dynamic cascade empirical mode decomposition is put forward for the weakness and aliasing out of uncertain factors. The fault features are much more obviously presented in different time scale by optimal intrinsic mode function (IMF) acquisition and the improvement of envelope solution method. The results of experiment show that weak faults can be obtained under much noise, and an effective method is provided for early fault prognosis and diagnosis.
abstractGer	Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure. It would be of benefit to prevent the fault deterioration if faults could be identified in early period, with the states of key parts obtained. Locomotive gear was chosen as the research object here, a method of inhibition mode mixing and characteristic frequency acquisition based on dynamic cascade empirical mode decomposition is put forward for the weakness and aliasing out of uncertain factors. The fault features are much more obviously presented in different time scale by optimal intrinsic mode function (IMF) acquisition and the improvement of envelope solution method. The results of experiment show that weak faults can be obtained under much noise, and an effective method is provided for early fault prognosis and diagnosis.
abstract_unstemmed	Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure. It would be of benefit to prevent the fault deterioration if faults could be identified in early period, with the states of key parts obtained. Locomotive gear was chosen as the research object here, a method of inhibition mode mixing and characteristic frequency acquisition based on dynamic cascade empirical mode decomposition is put forward for the weakness and aliasing out of uncertain factors. The fault features are much more obviously presented in different time scale by optimal intrinsic mode function (IMF) acquisition and the improvement of envelope solution method. The results of experiment show that weak faults can be obtained under much noise, and an effective method is provided for early fault prognosis and diagnosis.
collection_details	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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	Research of acquisition and prediction method in early weak information of locomotive traction system
url	https://doi.org/10.1049/joe.2018.9058 https://doaj.org/article/328a527d155046ba95ee2ccc4e25fccc https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9058 https://doaj.org/toc/2051-3305
remote_bool	true
author2	Siwen Li Shaopu Yang Wentao Song Yonggang Jiao
author2Str	Siwen Li Shaopu Yang Wentao Song Yonggang Jiao
ppnlink	75682270X
callnumber-subject	TA - General and Civil Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1049/joe.2018.9058
callnumber-a	TA1-2040
up_date	2024-07-04T01:19:21.996Z
_version_	1803609400409063424
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ049915126</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308145848.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1049/joe.2018.9058</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ049915126</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ328a527d155046ba95ee2ccc4e25fccc</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA1-2040</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Bin Ren</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Research of acquisition and prediction method in early weak information of locomotive traction system</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Many key parts of mechanical equipment gradually enter failure period with service, and failures happen in some of them, it will lead to serious consequences with economic loss and crash of machine and death of human if weak failures cannot be identified in time, which might degrade to major failure. It would be of benefit to prevent the fault deterioration if faults could be identified in early period, with the states of key parts obtained. Locomotive gear was chosen as the research object here, a method of inhibition mode mixing and characteristic frequency acquisition based on dynamic cascade empirical mode decomposition is put forward for the weakness and aliasing out of uncertain factors. The fault features are much more obviously presented in different time scale by optimal intrinsic mode function (IMF) acquisition and the improvement of envelope solution method. The results of experiment show that weak faults can be obtained under much noise, and an effective method is provided for early fault prognosis and diagnosis.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">locomotives</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">feature extraction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">fault diagnosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">gears</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">different time scale</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">fault features</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">uncertain factors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">dynamic cascade empirical mode decomposition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">characteristic frequency acquisition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">inhibition mode mixing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">locomotive gear</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">early period</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">fault deterioration</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">weak failures</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">crash</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">economic loss</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">failure period</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">mechanical equipment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">locomotive traction system</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">early weak information</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prediction method</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">early fault prognosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">weak faults</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">envelope solution method</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">optimal intrinsic mode function acquisition</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering (General). Civil engineering (General)</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Siwen Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shaopu Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wentao Song</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yonggang Jiao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">The Journal of Engineering</subfield><subfield code="d">Wiley, 2013</subfield><subfield code="g">(2019)</subfield><subfield code="w">(DE-627)75682270X</subfield><subfield code="w">(DE-600)2727074-9</subfield><subfield code="x">20513305</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">year:2019</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1049/joe.2018.9058</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/328a527d155046ba95ee2ccc4e25fccc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9058</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2051-3305</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="j">2019</subfield></datafield></record></collection>
score	7.4013395

Nicht das Richtige dabei?

Schreiben Sie uns!

Research of acquisition and prediction method in early weak information of locomotive traction system

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?