A supervised sparsity-based wavelet feature for bearing fault diagnosis

Abstract This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coe...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wang, Cong [verfasserIn] Gan, Meng [verfasserIn] Zhu, Chang’an [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Wavelet packet transform (WPT) Sparse coding Structured dictionary Supervised sparsity-based wavelet feature (SSWF) Machinery fault diagnosis

Übergeordnetes Werk:	Enthalten in: Journal of intelligent manufacturing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990, 30(2016), 1 vom: 01. Juli, Seite 229-239
Übergeordnetes Werk:	volume:30 ; year:2016 ; number:1 ; day:01 ; month:07 ; pages:229-239

Links:	Volltext

DOI / URN:	10.1007/s10845-016-1243-9

Katalog-ID:	SPR01368647X

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520			\|a Abstract This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. Experiments on two bearing cases are conducted to verify the advantages of SSWF in the detection of bearing faults.
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912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
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_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
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912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
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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_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
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_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
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912			\|a GBV_ILN_2065
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912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
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912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
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912			\|a GBV_ILN_2122
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912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
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912			\|a GBV_ILN_2548
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author_variant	c w cw m g mg c z cz
matchkey_str	article:15728145:2016----::spriesastbsdaeefaueoba
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publishDate	2016
allfields	10.1007/s10845-016-1243-9 doi (DE-627)SPR01368647X (SPR)s10845-016-1243-9-e DE-627 ger DE-627 rakwb eng 004 620 ASE 52.72 bkl Wang, Cong verfasserin aut A supervised sparsity-based wavelet feature for bearing fault diagnosis 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. Experiments on two bearing cases are conducted to verify the advantages of SSWF in the detection of bearing faults. Wavelet packet transform (WPT) (dpeaa)DE-He213 Sparse coding (dpeaa)DE-He213 Structured dictionary (dpeaa)DE-He213 Supervised sparsity-based wavelet feature (SSWF) (dpeaa)DE-He213 Machinery fault diagnosis (dpeaa)DE-He213 Gan, Meng verfasserin aut Zhu, Chang’an verfasserin aut Enthalten in Journal of intelligent manufacturing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 30(2016), 1 vom: 01. Juli, Seite 229-239 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:30 year:2016 number:1 day:01 month:07 pages:229-239 https://dx.doi.org/10.1007/s10845-016-1243-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4393 GBV_ILN_4700 52.72 ASE AR 30 2016 1 01 07 229-239
spelling	10.1007/s10845-016-1243-9 doi (DE-627)SPR01368647X (SPR)s10845-016-1243-9-e DE-627 ger DE-627 rakwb eng 004 620 ASE 52.72 bkl Wang, Cong verfasserin aut A supervised sparsity-based wavelet feature for bearing fault diagnosis 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. Experiments on two bearing cases are conducted to verify the advantages of SSWF in the detection of bearing faults. Wavelet packet transform (WPT) (dpeaa)DE-He213 Sparse coding (dpeaa)DE-He213 Structured dictionary (dpeaa)DE-He213 Supervised sparsity-based wavelet feature (SSWF) (dpeaa)DE-He213 Machinery fault diagnosis (dpeaa)DE-He213 Gan, Meng verfasserin aut Zhu, Chang’an verfasserin aut Enthalten in Journal of intelligent manufacturing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 30(2016), 1 vom: 01. Juli, Seite 229-239 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:30 year:2016 number:1 day:01 month:07 pages:229-239 https://dx.doi.org/10.1007/s10845-016-1243-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4393 GBV_ILN_4700 52.72 ASE AR 30 2016 1 01 07 229-239
allfields_unstemmed	10.1007/s10845-016-1243-9 doi (DE-627)SPR01368647X (SPR)s10845-016-1243-9-e DE-627 ger DE-627 rakwb eng 004 620 ASE 52.72 bkl Wang, Cong verfasserin aut A supervised sparsity-based wavelet feature for bearing fault diagnosis 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. Experiments on two bearing cases are conducted to verify the advantages of SSWF in the detection of bearing faults. Wavelet packet transform (WPT) (dpeaa)DE-He213 Sparse coding (dpeaa)DE-He213 Structured dictionary (dpeaa)DE-He213 Supervised sparsity-based wavelet feature (SSWF) (dpeaa)DE-He213 Machinery fault diagnosis (dpeaa)DE-He213 Gan, Meng verfasserin aut Zhu, Chang’an verfasserin aut Enthalten in Journal of intelligent manufacturing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 30(2016), 1 vom: 01. Juli, Seite 229-239 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:30 year:2016 number:1 day:01 month:07 pages:229-239 https://dx.doi.org/10.1007/s10845-016-1243-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4393 GBV_ILN_4700 52.72 ASE AR 30 2016 1 01 07 229-239
allfieldsGer	10.1007/s10845-016-1243-9 doi (DE-627)SPR01368647X (SPR)s10845-016-1243-9-e DE-627 ger DE-627 rakwb eng 004 620 ASE 52.72 bkl Wang, Cong verfasserin aut A supervised sparsity-based wavelet feature for bearing fault diagnosis 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. Experiments on two bearing cases are conducted to verify the advantages of SSWF in the detection of bearing faults. Wavelet packet transform (WPT) (dpeaa)DE-He213 Sparse coding (dpeaa)DE-He213 Structured dictionary (dpeaa)DE-He213 Supervised sparsity-based wavelet feature (SSWF) (dpeaa)DE-He213 Machinery fault diagnosis (dpeaa)DE-He213 Gan, Meng verfasserin aut Zhu, Chang’an verfasserin aut Enthalten in Journal of intelligent manufacturing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 30(2016), 1 vom: 01. Juli, Seite 229-239 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:30 year:2016 number:1 day:01 month:07 pages:229-239 https://dx.doi.org/10.1007/s10845-016-1243-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4393 GBV_ILN_4700 52.72 ASE AR 30 2016 1 01 07 229-239
allfieldsSound	10.1007/s10845-016-1243-9 doi (DE-627)SPR01368647X (SPR)s10845-016-1243-9-e DE-627 ger DE-627 rakwb eng 004 620 ASE 52.72 bkl Wang, Cong verfasserin aut A supervised sparsity-based wavelet feature for bearing fault diagnosis 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. Experiments on two bearing cases are conducted to verify the advantages of SSWF in the detection of bearing faults. Wavelet packet transform (WPT) (dpeaa)DE-He213 Sparse coding (dpeaa)DE-He213 Structured dictionary (dpeaa)DE-He213 Supervised sparsity-based wavelet feature (SSWF) (dpeaa)DE-He213 Machinery fault diagnosis (dpeaa)DE-He213 Gan, Meng verfasserin aut Zhu, Chang’an verfasserin aut Enthalten in Journal of intelligent manufacturing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 30(2016), 1 vom: 01. Juli, Seite 229-239 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:30 year:2016 number:1 day:01 month:07 pages:229-239 https://dx.doi.org/10.1007/s10845-016-1243-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4393 GBV_ILN_4700 52.72 ASE AR 30 2016 1 01 07 229-239
language	English
source	Enthalten in Journal of intelligent manufacturing 30(2016), 1 vom: 01. Juli, Seite 229-239 volume:30 year:2016 number:1 day:01 month:07 pages:229-239
sourceStr	Enthalten in Journal of intelligent manufacturing 30(2016), 1 vom: 01. Juli, Seite 229-239 volume:30 year:2016 number:1 day:01 month:07 pages:229-239
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Wavelet packet transform (WPT) Sparse coding Structured dictionary Supervised sparsity-based wavelet feature (SSWF) Machinery fault diagnosis
dewey-raw	004
isfreeaccess_bool	false
container_title	Journal of intelligent manufacturing
authorswithroles_txt_mv	Wang, Cong @@aut@@ Gan, Meng @@aut@@ Zhu, Chang’an @@aut@@
publishDateDaySort_date	2016-07-01T00:00:00Z
hierarchy_top_id	315293519
dewey-sort	14
id	SPR01368647X
language_de	englisch
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SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. 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author	Wang, Cong
spellingShingle	Wang, Cong ddc 004 bkl 52.72 misc Wavelet packet transform (WPT) misc Sparse coding misc Structured dictionary misc Supervised sparsity-based wavelet feature (SSWF) misc Machinery fault diagnosis A supervised sparsity-based wavelet feature for bearing fault diagnosis
authorStr	Wang, Cong
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topic_title	004 620 ASE 52.72 bkl A supervised sparsity-based wavelet feature for bearing fault diagnosis Wavelet packet transform (WPT) (dpeaa)DE-He213 Sparse coding (dpeaa)DE-He213 Structured dictionary (dpeaa)DE-He213 Supervised sparsity-based wavelet feature (SSWF) (dpeaa)DE-He213 Machinery fault diagnosis (dpeaa)DE-He213
topic	ddc 004 bkl 52.72 misc Wavelet packet transform (WPT) misc Sparse coding misc Structured dictionary misc Supervised sparsity-based wavelet feature (SSWF) misc Machinery fault diagnosis
topic_unstemmed	ddc 004 bkl 52.72 misc Wavelet packet transform (WPT) misc Sparse coding misc Structured dictionary misc Supervised sparsity-based wavelet feature (SSWF) misc Machinery fault diagnosis
topic_browse	ddc 004 bkl 52.72 misc Wavelet packet transform (WPT) misc Sparse coding misc Structured dictionary misc Supervised sparsity-based wavelet feature (SSWF) misc Machinery fault diagnosis
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title	A supervised sparsity-based wavelet feature for bearing fault diagnosis
ctrlnum	(DE-627)SPR01368647X (SPR)s10845-016-1243-9-e
title_full	A supervised sparsity-based wavelet feature for bearing fault diagnosis
author_sort	Wang, Cong
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author_browse	Wang, Cong Gan, Meng Zhu, Chang’an
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title_sort	supervised sparsity-based wavelet feature for bearing fault diagnosis
title_auth	A supervised sparsity-based wavelet feature for bearing fault diagnosis
abstract	Abstract This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. Experiments on two bearing cases are conducted to verify the advantages of SSWF in the detection of bearing faults.
abstractGer	Abstract This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. Experiments on two bearing cases are conducted to verify the advantages of SSWF in the detection of bearing faults.
abstract_unstemmed	Abstract This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. Experiments on two bearing cases are conducted to verify the advantages of SSWF in the detection of bearing faults.
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container_issue	1
title_short	A supervised sparsity-based wavelet feature for bearing fault diagnosis
url	https://dx.doi.org/10.1007/s10845-016-1243-9
remote_bool	true
author2	Gan, Meng Zhu, Chang’an
author2Str	Gan, Meng Zhu, Chang’an
ppnlink	315293519
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s10845-016-1243-9
up_date	2024-07-03T21:28:29.034Z
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A supervised sparsity-based wavelet feature for bearing fault diagnosis

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