Cross-Attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes
Vibration signals of wind turbine gearboxes are often collected under various sampling frequencies. However, most traditional domain adaptation methods, which are applied to improve fault diagnosing accuracy with limited or unlabeled datasets, only consider a single source domain with the same sampl...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Qikang [verfasserIn] Tang, Baoping [verfasserIn] Deng, Lei [verfasserIn] Xiong, Peng [verfasserIn] Zhao, Minghang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Measurement - Amsterdam [u.a.] : Elsevier Science, 1983, 200 |
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| Übergeordnetes Werk: |
volume:200 |
| DOI / URN: |
10.1016/j.measurement.2022.111570 |
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| Katalog-ID: |
ELV009987053 |
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| 245 | 1 | 0 | |a Cross-Attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes |
| 264 | 1 | |c 2022 | |
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| 337 | |a Computermedien |b c |2 rdamedia | ||
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| 520 | |a Vibration signals of wind turbine gearboxes are often collected under various sampling frequencies. However, most traditional domain adaptation methods, which are applied to improve fault diagnosing accuracy with limited or unlabeled datasets, only consider a single source domain with the same sampling frequency. In this paper, a novel domain-invariant feature learning method, i.e., cross-attribute adaptation networks (CAAN), is developed, in which each source domain with a particular sampling frequency has individual feature learning and adaptation modules. Specifically, a multi-branch framework with the attention mechanism is developed to learn and weigh the characteristics of multiple sampling-frequency data separately. Then, the discrepancies between multiple classifiers and the domains are minimized to build a precise decision boundary. Extensive experimental analysis on real wind turbine gearboxes datasets is performed to demonstrate the effectiveness and advantage of the proposed CAAN. | ||
| 650 | 4 | |a Cross-attribute adaptation networks | |
| 650 | 4 | |a Attention mechanism | |
| 650 | 4 | |a Fault diagnosis | |
| 650 | 4 | |a Wind turbine gearboxes | |
| 700 | 1 | |a Tang, Baoping |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Xiong, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Minghang |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Measurement |d Amsterdam [u.a.] : Elsevier Science, 1983 |g 200 |h Online-Ressource |w (DE-627)320404927 |w (DE-600)2000550-7 |w (DE-576)259484342 |7 nnns |
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10.1016/j.measurement.2022.111570 doi (DE-627)ELV009987053 (ELSEVIER)S0263-2241(22)00785-0 DE-627 ger DE-627 rda eng 660 VZ 50.21 bkl Li, Qikang verfasserin aut Cross-Attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Vibration signals of wind turbine gearboxes are often collected under various sampling frequencies. However, most traditional domain adaptation methods, which are applied to improve fault diagnosing accuracy with limited or unlabeled datasets, only consider a single source domain with the same sampling frequency. In this paper, a novel domain-invariant feature learning method, i.e., cross-attribute adaptation networks (CAAN), is developed, in which each source domain with a particular sampling frequency has individual feature learning and adaptation modules. Specifically, a multi-branch framework with the attention mechanism is developed to learn and weigh the characteristics of multiple sampling-frequency data separately. Then, the discrepancies between multiple classifiers and the domains are minimized to build a precise decision boundary. Extensive experimental analysis on real wind turbine gearboxes datasets is performed to demonstrate the effectiveness and advantage of the proposed CAAN. Cross-attribute adaptation networks Attention mechanism Fault diagnosis Wind turbine gearboxes Tang, Baoping verfasserin aut Deng, Lei verfasserin aut Xiong, Peng verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 200 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:200 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik VZ AR 200 |
| spelling |
10.1016/j.measurement.2022.111570 doi (DE-627)ELV009987053 (ELSEVIER)S0263-2241(22)00785-0 DE-627 ger DE-627 rda eng 660 VZ 50.21 bkl Li, Qikang verfasserin aut Cross-Attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Vibration signals of wind turbine gearboxes are often collected under various sampling frequencies. However, most traditional domain adaptation methods, which are applied to improve fault diagnosing accuracy with limited or unlabeled datasets, only consider a single source domain with the same sampling frequency. In this paper, a novel domain-invariant feature learning method, i.e., cross-attribute adaptation networks (CAAN), is developed, in which each source domain with a particular sampling frequency has individual feature learning and adaptation modules. Specifically, a multi-branch framework with the attention mechanism is developed to learn and weigh the characteristics of multiple sampling-frequency data separately. Then, the discrepancies between multiple classifiers and the domains are minimized to build a precise decision boundary. Extensive experimental analysis on real wind turbine gearboxes datasets is performed to demonstrate the effectiveness and advantage of the proposed CAAN. Cross-attribute adaptation networks Attention mechanism Fault diagnosis Wind turbine gearboxes Tang, Baoping verfasserin aut Deng, Lei verfasserin aut Xiong, Peng verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 200 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:200 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik VZ AR 200 |
| allfields_unstemmed |
10.1016/j.measurement.2022.111570 doi (DE-627)ELV009987053 (ELSEVIER)S0263-2241(22)00785-0 DE-627 ger DE-627 rda eng 660 VZ 50.21 bkl Li, Qikang verfasserin aut Cross-Attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Vibration signals of wind turbine gearboxes are often collected under various sampling frequencies. However, most traditional domain adaptation methods, which are applied to improve fault diagnosing accuracy with limited or unlabeled datasets, only consider a single source domain with the same sampling frequency. In this paper, a novel domain-invariant feature learning method, i.e., cross-attribute adaptation networks (CAAN), is developed, in which each source domain with a particular sampling frequency has individual feature learning and adaptation modules. Specifically, a multi-branch framework with the attention mechanism is developed to learn and weigh the characteristics of multiple sampling-frequency data separately. Then, the discrepancies between multiple classifiers and the domains are minimized to build a precise decision boundary. Extensive experimental analysis on real wind turbine gearboxes datasets is performed to demonstrate the effectiveness and advantage of the proposed CAAN. Cross-attribute adaptation networks Attention mechanism Fault diagnosis Wind turbine gearboxes Tang, Baoping verfasserin aut Deng, Lei verfasserin aut Xiong, Peng verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 200 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:200 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik VZ AR 200 |
| allfieldsGer |
10.1016/j.measurement.2022.111570 doi (DE-627)ELV009987053 (ELSEVIER)S0263-2241(22)00785-0 DE-627 ger DE-627 rda eng 660 VZ 50.21 bkl Li, Qikang verfasserin aut Cross-Attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Vibration signals of wind turbine gearboxes are often collected under various sampling frequencies. However, most traditional domain adaptation methods, which are applied to improve fault diagnosing accuracy with limited or unlabeled datasets, only consider a single source domain with the same sampling frequency. In this paper, a novel domain-invariant feature learning method, i.e., cross-attribute adaptation networks (CAAN), is developed, in which each source domain with a particular sampling frequency has individual feature learning and adaptation modules. Specifically, a multi-branch framework with the attention mechanism is developed to learn and weigh the characteristics of multiple sampling-frequency data separately. Then, the discrepancies between multiple classifiers and the domains are minimized to build a precise decision boundary. Extensive experimental analysis on real wind turbine gearboxes datasets is performed to demonstrate the effectiveness and advantage of the proposed CAAN. Cross-attribute adaptation networks Attention mechanism Fault diagnosis Wind turbine gearboxes Tang, Baoping verfasserin aut Deng, Lei verfasserin aut Xiong, Peng verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 200 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:200 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik VZ AR 200 |
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10.1016/j.measurement.2022.111570 doi (DE-627)ELV009987053 (ELSEVIER)S0263-2241(22)00785-0 DE-627 ger DE-627 rda eng 660 VZ 50.21 bkl Li, Qikang verfasserin aut Cross-Attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Vibration signals of wind turbine gearboxes are often collected under various sampling frequencies. However, most traditional domain adaptation methods, which are applied to improve fault diagnosing accuracy with limited or unlabeled datasets, only consider a single source domain with the same sampling frequency. In this paper, a novel domain-invariant feature learning method, i.e., cross-attribute adaptation networks (CAAN), is developed, in which each source domain with a particular sampling frequency has individual feature learning and adaptation modules. Specifically, a multi-branch framework with the attention mechanism is developed to learn and weigh the characteristics of multiple sampling-frequency data separately. Then, the discrepancies between multiple classifiers and the domains are minimized to build a precise decision boundary. Extensive experimental analysis on real wind turbine gearboxes datasets is performed to demonstrate the effectiveness and advantage of the proposed CAAN. Cross-attribute adaptation networks Attention mechanism Fault diagnosis Wind turbine gearboxes Tang, Baoping verfasserin aut Deng, Lei verfasserin aut Xiong, Peng verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 200 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:200 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik VZ AR 200 |
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Li, Qikang Tang, Baoping Deng, Lei Xiong, Peng Zhao, Minghang |
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Li, Qikang |
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10.1016/j.measurement.2022.111570 |
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cross-attribute adaptation networks: distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes |
| title_auth |
Cross-Attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes |
| abstract |
Vibration signals of wind turbine gearboxes are often collected under various sampling frequencies. However, most traditional domain adaptation methods, which are applied to improve fault diagnosing accuracy with limited or unlabeled datasets, only consider a single source domain with the same sampling frequency. In this paper, a novel domain-invariant feature learning method, i.e., cross-attribute adaptation networks (CAAN), is developed, in which each source domain with a particular sampling frequency has individual feature learning and adaptation modules. Specifically, a multi-branch framework with the attention mechanism is developed to learn and weigh the characteristics of multiple sampling-frequency data separately. Then, the discrepancies between multiple classifiers and the domains are minimized to build a precise decision boundary. Extensive experimental analysis on real wind turbine gearboxes datasets is performed to demonstrate the effectiveness and advantage of the proposed CAAN. |
| abstractGer |
Vibration signals of wind turbine gearboxes are often collected under various sampling frequencies. However, most traditional domain adaptation methods, which are applied to improve fault diagnosing accuracy with limited or unlabeled datasets, only consider a single source domain with the same sampling frequency. In this paper, a novel domain-invariant feature learning method, i.e., cross-attribute adaptation networks (CAAN), is developed, in which each source domain with a particular sampling frequency has individual feature learning and adaptation modules. Specifically, a multi-branch framework with the attention mechanism is developed to learn and weigh the characteristics of multiple sampling-frequency data separately. Then, the discrepancies between multiple classifiers and the domains are minimized to build a precise decision boundary. Extensive experimental analysis on real wind turbine gearboxes datasets is performed to demonstrate the effectiveness and advantage of the proposed CAAN. |
| abstract_unstemmed |
Vibration signals of wind turbine gearboxes are often collected under various sampling frequencies. However, most traditional domain adaptation methods, which are applied to improve fault diagnosing accuracy with limited or unlabeled datasets, only consider a single source domain with the same sampling frequency. In this paper, a novel domain-invariant feature learning method, i.e., cross-attribute adaptation networks (CAAN), is developed, in which each source domain with a particular sampling frequency has individual feature learning and adaptation modules. Specifically, a multi-branch framework with the attention mechanism is developed to learn and weigh the characteristics of multiple sampling-frequency data separately. Then, the discrepancies between multiple classifiers and the domains are minimized to build a precise decision boundary. Extensive experimental analysis on real wind turbine gearboxes datasets is performed to demonstrate the effectiveness and advantage of the proposed CAAN. |
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| title_short |
Cross-Attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes |
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Tang, Baoping Deng, Lei Xiong, Peng Zhao, Minghang |
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