Wavelet transform for rotary machine fault diagnosis:10 years revisited
As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform (WT) has shown its great potential in rotary machine fault diagnosis, characterized by continued development and innovative new applications. In traditional fault diagnosis, WT has been widely used for fault fe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yan, Ruqiang [verfasserIn] Shang, Zuogang [verfasserIn] Xu, Hong [verfasserIn] Wen, Jingcheng [verfasserIn] Zhao, Zhibin [verfasserIn] Chen, Xuefeng [verfasserIn] Gao, Robert X. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Mechanical systems and signal processing - Amsterdam [u.a.] : Elsevier, 1987, 200 |
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| Übergeordnetes Werk: |
volume:200 |
| DOI / URN: |
10.1016/j.ymssp.2023.110545 |
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| Katalog-ID: |
ELV061592935 |
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| 245 | 1 | 0 | |a Wavelet transform for rotary machine fault diagnosis:10 years revisited |
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| 520 | |a As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform (WT) has shown its great potential in rotary machine fault diagnosis, characterized by continued development and innovative new applications. In traditional fault diagnosis, WT has been widely used for fault feature extraction and extensively studied for performance improvement. With the emergence of data-driven intelligent fault diagnosis, especially deep learning techniques, WT has attracted renewed attention for its ability of adding interpretability into the intelligent diagnosis models. This paper aims to highlight the advancement of WT-based fault diagnosis research over the last decade. Toward this end, a comprehensive overview of WT method is given, followed by a summary of WT for fault diagnosis from two perspectives: traditional fault diagnosis and intelligent fault diagnosis. Finally, future research trends are discussed, including benchmarking, wavelet base design, integration with other methods, and enhancement through deep learning. | ||
| 650 | 4 | |a Wavelet transform | |
| 650 | 4 | |a Traditional fault diagnosis | |
| 650 | 4 | |a Intelligent fault diagnosis | |
| 700 | 1 | |a Shang, Zuogang |e verfasserin |0 (orcid)0000-0002-2608-4068 |4 aut | |
| 700 | 1 | |a Xu, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Wen, Jingcheng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Zhibin |e verfasserin |0 (orcid)0000-0003-4180-7137 |4 aut | |
| 700 | 1 | |a Chen, Xuefeng |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Robert X. |e verfasserin |0 (orcid)0000-0003-3595-3728 |4 aut | |
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10.1016/j.ymssp.2023.110545 doi (DE-627)ELV061592935 (ELSEVIER)S0888-3270(23)00453-3 DE-627 ger DE-627 rda eng 004 VZ 50.32 bkl 50.16 bkl Yan, Ruqiang verfasserin (orcid)0000-0002-1250-4084 aut Wavelet transform for rotary machine fault diagnosis:10 years revisited 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform (WT) has shown its great potential in rotary machine fault diagnosis, characterized by continued development and innovative new applications. In traditional fault diagnosis, WT has been widely used for fault feature extraction and extensively studied for performance improvement. With the emergence of data-driven intelligent fault diagnosis, especially deep learning techniques, WT has attracted renewed attention for its ability of adding interpretability into the intelligent diagnosis models. This paper aims to highlight the advancement of WT-based fault diagnosis research over the last decade. Toward this end, a comprehensive overview of WT method is given, followed by a summary of WT for fault diagnosis from two perspectives: traditional fault diagnosis and intelligent fault diagnosis. Finally, future research trends are discussed, including benchmarking, wavelet base design, integration with other methods, and enhancement through deep learning. Wavelet transform Traditional fault diagnosis Intelligent fault diagnosis Shang, Zuogang verfasserin (orcid)0000-0002-2608-4068 aut Xu, Hong verfasserin aut Wen, Jingcheng verfasserin aut Zhao, Zhibin verfasserin (orcid)0000-0003-4180-7137 aut Chen, Xuefeng verfasserin aut Gao, Robert X. verfasserin (orcid)0000-0003-3595-3728 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 200 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:200 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.16 Technische Zuverlässigkeit Instandhaltung VZ AR 200 |
| spelling |
10.1016/j.ymssp.2023.110545 doi (DE-627)ELV061592935 (ELSEVIER)S0888-3270(23)00453-3 DE-627 ger DE-627 rda eng 004 VZ 50.32 bkl 50.16 bkl Yan, Ruqiang verfasserin (orcid)0000-0002-1250-4084 aut Wavelet transform for rotary machine fault diagnosis:10 years revisited 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform (WT) has shown its great potential in rotary machine fault diagnosis, characterized by continued development and innovative new applications. In traditional fault diagnosis, WT has been widely used for fault feature extraction and extensively studied for performance improvement. With the emergence of data-driven intelligent fault diagnosis, especially deep learning techniques, WT has attracted renewed attention for its ability of adding interpretability into the intelligent diagnosis models. This paper aims to highlight the advancement of WT-based fault diagnosis research over the last decade. Toward this end, a comprehensive overview of WT method is given, followed by a summary of WT for fault diagnosis from two perspectives: traditional fault diagnosis and intelligent fault diagnosis. Finally, future research trends are discussed, including benchmarking, wavelet base design, integration with other methods, and enhancement through deep learning. Wavelet transform Traditional fault diagnosis Intelligent fault diagnosis Shang, Zuogang verfasserin (orcid)0000-0002-2608-4068 aut Xu, Hong verfasserin aut Wen, Jingcheng verfasserin aut Zhao, Zhibin verfasserin (orcid)0000-0003-4180-7137 aut Chen, Xuefeng verfasserin aut Gao, Robert X. verfasserin (orcid)0000-0003-3595-3728 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 200 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:200 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.16 Technische Zuverlässigkeit Instandhaltung VZ AR 200 |
| allfields_unstemmed |
10.1016/j.ymssp.2023.110545 doi (DE-627)ELV061592935 (ELSEVIER)S0888-3270(23)00453-3 DE-627 ger DE-627 rda eng 004 VZ 50.32 bkl 50.16 bkl Yan, Ruqiang verfasserin (orcid)0000-0002-1250-4084 aut Wavelet transform for rotary machine fault diagnosis:10 years revisited 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform (WT) has shown its great potential in rotary machine fault diagnosis, characterized by continued development and innovative new applications. In traditional fault diagnosis, WT has been widely used for fault feature extraction and extensively studied for performance improvement. With the emergence of data-driven intelligent fault diagnosis, especially deep learning techniques, WT has attracted renewed attention for its ability of adding interpretability into the intelligent diagnosis models. This paper aims to highlight the advancement of WT-based fault diagnosis research over the last decade. Toward this end, a comprehensive overview of WT method is given, followed by a summary of WT for fault diagnosis from two perspectives: traditional fault diagnosis and intelligent fault diagnosis. Finally, future research trends are discussed, including benchmarking, wavelet base design, integration with other methods, and enhancement through deep learning. Wavelet transform Traditional fault diagnosis Intelligent fault diagnosis Shang, Zuogang verfasserin (orcid)0000-0002-2608-4068 aut Xu, Hong verfasserin aut Wen, Jingcheng verfasserin aut Zhao, Zhibin verfasserin (orcid)0000-0003-4180-7137 aut Chen, Xuefeng verfasserin aut Gao, Robert X. verfasserin (orcid)0000-0003-3595-3728 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 200 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:200 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.16 Technische Zuverlässigkeit Instandhaltung VZ AR 200 |
| allfieldsGer |
10.1016/j.ymssp.2023.110545 doi (DE-627)ELV061592935 (ELSEVIER)S0888-3270(23)00453-3 DE-627 ger DE-627 rda eng 004 VZ 50.32 bkl 50.16 bkl Yan, Ruqiang verfasserin (orcid)0000-0002-1250-4084 aut Wavelet transform for rotary machine fault diagnosis:10 years revisited 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform (WT) has shown its great potential in rotary machine fault diagnosis, characterized by continued development and innovative new applications. In traditional fault diagnosis, WT has been widely used for fault feature extraction and extensively studied for performance improvement. With the emergence of data-driven intelligent fault diagnosis, especially deep learning techniques, WT has attracted renewed attention for its ability of adding interpretability into the intelligent diagnosis models. This paper aims to highlight the advancement of WT-based fault diagnosis research over the last decade. Toward this end, a comprehensive overview of WT method is given, followed by a summary of WT for fault diagnosis from two perspectives: traditional fault diagnosis and intelligent fault diagnosis. Finally, future research trends are discussed, including benchmarking, wavelet base design, integration with other methods, and enhancement through deep learning. Wavelet transform Traditional fault diagnosis Intelligent fault diagnosis Shang, Zuogang verfasserin (orcid)0000-0002-2608-4068 aut Xu, Hong verfasserin aut Wen, Jingcheng verfasserin aut Zhao, Zhibin verfasserin (orcid)0000-0003-4180-7137 aut Chen, Xuefeng verfasserin aut Gao, Robert X. verfasserin (orcid)0000-0003-3595-3728 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 200 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:200 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.16 Technische Zuverlässigkeit Instandhaltung VZ AR 200 |
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10.1016/j.ymssp.2023.110545 doi (DE-627)ELV061592935 (ELSEVIER)S0888-3270(23)00453-3 DE-627 ger DE-627 rda eng 004 VZ 50.32 bkl 50.16 bkl Yan, Ruqiang verfasserin (orcid)0000-0002-1250-4084 aut Wavelet transform for rotary machine fault diagnosis:10 years revisited 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform (WT) has shown its great potential in rotary machine fault diagnosis, characterized by continued development and innovative new applications. In traditional fault diagnosis, WT has been widely used for fault feature extraction and extensively studied for performance improvement. With the emergence of data-driven intelligent fault diagnosis, especially deep learning techniques, WT has attracted renewed attention for its ability of adding interpretability into the intelligent diagnosis models. This paper aims to highlight the advancement of WT-based fault diagnosis research over the last decade. Toward this end, a comprehensive overview of WT method is given, followed by a summary of WT for fault diagnosis from two perspectives: traditional fault diagnosis and intelligent fault diagnosis. Finally, future research trends are discussed, including benchmarking, wavelet base design, integration with other methods, and enhancement through deep learning. Wavelet transform Traditional fault diagnosis Intelligent fault diagnosis Shang, Zuogang verfasserin (orcid)0000-0002-2608-4068 aut Xu, Hong verfasserin aut Wen, Jingcheng verfasserin aut Zhao, Zhibin verfasserin (orcid)0000-0003-4180-7137 aut Chen, Xuefeng verfasserin aut Gao, Robert X. verfasserin (orcid)0000-0003-3595-3728 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 200 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:200 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.16 Technische Zuverlässigkeit Instandhaltung VZ AR 200 |
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004 VZ 50.32 bkl 50.16 bkl Wavelet transform for rotary machine fault diagnosis:10 years revisited Wavelet transform Traditional fault diagnosis Intelligent fault diagnosis |
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Wavelet transform for rotary machine fault diagnosis:10 years revisited |
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Yan, Ruqiang Shang, Zuogang Xu, Hong Wen, Jingcheng Zhao, Zhibin Chen, Xuefeng Gao, Robert X. |
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wavelet transform for rotary machine fault diagnosis:10 years revisited |
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Wavelet transform for rotary machine fault diagnosis:10 years revisited |
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As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform (WT) has shown its great potential in rotary machine fault diagnosis, characterized by continued development and innovative new applications. In traditional fault diagnosis, WT has been widely used for fault feature extraction and extensively studied for performance improvement. With the emergence of data-driven intelligent fault diagnosis, especially deep learning techniques, WT has attracted renewed attention for its ability of adding interpretability into the intelligent diagnosis models. This paper aims to highlight the advancement of WT-based fault diagnosis research over the last decade. Toward this end, a comprehensive overview of WT method is given, followed by a summary of WT for fault diagnosis from two perspectives: traditional fault diagnosis and intelligent fault diagnosis. Finally, future research trends are discussed, including benchmarking, wavelet base design, integration with other methods, and enhancement through deep learning. |
| abstractGer |
As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform (WT) has shown its great potential in rotary machine fault diagnosis, characterized by continued development and innovative new applications. In traditional fault diagnosis, WT has been widely used for fault feature extraction and extensively studied for performance improvement. With the emergence of data-driven intelligent fault diagnosis, especially deep learning techniques, WT has attracted renewed attention for its ability of adding interpretability into the intelligent diagnosis models. This paper aims to highlight the advancement of WT-based fault diagnosis research over the last decade. Toward this end, a comprehensive overview of WT method is given, followed by a summary of WT for fault diagnosis from two perspectives: traditional fault diagnosis and intelligent fault diagnosis. Finally, future research trends are discussed, including benchmarking, wavelet base design, integration with other methods, and enhancement through deep learning. |
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As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform (WT) has shown its great potential in rotary machine fault diagnosis, characterized by continued development and innovative new applications. In traditional fault diagnosis, WT has been widely used for fault feature extraction and extensively studied for performance improvement. With the emergence of data-driven intelligent fault diagnosis, especially deep learning techniques, WT has attracted renewed attention for its ability of adding interpretability into the intelligent diagnosis models. This paper aims to highlight the advancement of WT-based fault diagnosis research over the last decade. Toward this end, a comprehensive overview of WT method is given, followed by a summary of WT for fault diagnosis from two perspectives: traditional fault diagnosis and intelligent fault diagnosis. Finally, future research trends are discussed, including benchmarking, wavelet base design, integration with other methods, and enhancement through deep learning. |
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