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] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
Wavelet packet transform (WPT) |
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| Anmerkung: |
© Springer Science+Business Media New York 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent manufacturing - Springer US, 1990, 30(2016), 1 vom: 01. Juli, Seite 229-239 |
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| Übergeordnetes Werk: |
volume:30 ; year:2016 ; number:1 ; day:01 ; month:07 ; pages:229-239 |
| Links: |
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| DOI / URN: |
10.1007/s10845-016-1243-9 |
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OLC206677782X |
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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. | ||
| 650 | 4 | |a Wavelet packet transform (WPT) | |
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| 700 | 1 | |a Gan, Meng |4 aut | |
| 700 | 1 | |a Zhu, Chang’an |4 aut | |
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10.1007/s10845-016-1243-9 doi (DE-627)OLC206677782X (DE-He213)s10845-016-1243-9-p DE-627 ger DE-627 rakwb eng 620 004 VZ Wang, Cong verfasserin aut A supervised sparsity-based wavelet feature for bearing fault diagnosis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 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) Sparse coding Structured dictionary Supervised sparsity-based wavelet feature (SSWF) Machinery fault diagnosis Gan, Meng aut Zhu, Chang’an aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 30(2016), 1 vom: 01. Juli, Seite 229-239 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:30 year:2016 number:1 day:01 month:07 pages:229-239 https://doi.org/10.1007/s10845-016-1243-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 30 2016 1 01 07 229-239 |
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10.1007/s10845-016-1243-9 doi (DE-627)OLC206677782X (DE-He213)s10845-016-1243-9-p DE-627 ger DE-627 rakwb eng 620 004 VZ Wang, Cong verfasserin aut A supervised sparsity-based wavelet feature for bearing fault diagnosis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 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) Sparse coding Structured dictionary Supervised sparsity-based wavelet feature (SSWF) Machinery fault diagnosis Gan, Meng aut Zhu, Chang’an aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 30(2016), 1 vom: 01. Juli, Seite 229-239 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:30 year:2016 number:1 day:01 month:07 pages:229-239 https://doi.org/10.1007/s10845-016-1243-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 30 2016 1 01 07 229-239 |
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10.1007/s10845-016-1243-9 doi (DE-627)OLC206677782X (DE-He213)s10845-016-1243-9-p DE-627 ger DE-627 rakwb eng 620 004 VZ Wang, Cong verfasserin aut A supervised sparsity-based wavelet feature for bearing fault diagnosis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 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) Sparse coding Structured dictionary Supervised sparsity-based wavelet feature (SSWF) Machinery fault diagnosis Gan, Meng aut Zhu, Chang’an aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 30(2016), 1 vom: 01. Juli, Seite 229-239 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:30 year:2016 number:1 day:01 month:07 pages:229-239 https://doi.org/10.1007/s10845-016-1243-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 30 2016 1 01 07 229-239 |
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10.1007/s10845-016-1243-9 doi (DE-627)OLC206677782X (DE-He213)s10845-016-1243-9-p DE-627 ger DE-627 rakwb eng 620 004 VZ Wang, Cong verfasserin aut A supervised sparsity-based wavelet feature for bearing fault diagnosis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 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) Sparse coding Structured dictionary Supervised sparsity-based wavelet feature (SSWF) Machinery fault diagnosis Gan, Meng aut Zhu, Chang’an aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 30(2016), 1 vom: 01. Juli, Seite 229-239 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:30 year:2016 number:1 day:01 month:07 pages:229-239 https://doi.org/10.1007/s10845-016-1243-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 30 2016 1 01 07 229-239 |
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10.1007/s10845-016-1243-9 doi (DE-627)OLC206677782X (DE-He213)s10845-016-1243-9-p DE-627 ger DE-627 rakwb eng 620 004 VZ Wang, Cong verfasserin aut A supervised sparsity-based wavelet feature for bearing fault diagnosis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 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) Sparse coding Structured dictionary Supervised sparsity-based wavelet feature (SSWF) Machinery fault diagnosis Gan, Meng aut Zhu, Chang’an aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 30(2016), 1 vom: 01. Juli, Seite 229-239 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:30 year:2016 number:1 day:01 month:07 pages:229-239 https://doi.org/10.1007/s10845-016-1243-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 30 2016 1 01 07 229-239 |
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A supervised sparsity-based wavelet feature for bearing fault diagnosis |
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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. © Springer Science+Business Media New York 2016 |
| 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. © Springer Science+Business Media New York 2016 |
| 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. © Springer Science+Business Media New York 2016 |
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A supervised sparsity-based wavelet feature for bearing fault diagnosis |
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Gan, Meng Zhu, Chang’an |
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