A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions
Purpose Due to the influence of external factors such as noise, the operating conditions of rotating machinery in actual operation are not constant, and it is difficult to obtain high diagnostic accuracy using differently distributed training data and test data for fault diagnosis. Methods To solve...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shang, Zhiwu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© Krishtel eMaging Solutions Private Limited 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of vibration engineering & technologies - Singapore : Springer Singapore, 2018, 12(2022), 1 vom: 24. Dez., Seite 1-17 |
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| Übergeordnetes Werk: |
volume:12 ; year:2022 ; number:1 ; day:24 ; month:12 ; pages:1-17 |
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| DOI / URN: |
10.1007/s42417-022-00823-2 |
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| Katalog-ID: |
SPR054642752 |
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| 245 | 1 | 2 | |a A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions |
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| 520 | |a Purpose Due to the influence of external factors such as noise, the operating conditions of rotating machinery in actual operation are not constant, and it is difficult to obtain high diagnostic accuracy using differently distributed training data and test data for fault diagnosis. Methods To solve the problem of fault diagnosis under variable working conditions, we come up with a fault diagnosis approach on the account of Inception V1 module with self-attention mechanism for domain adversarial transfer network (IS-DATN). First, a feature extractor based on the Inception V1 module and the self-attention mechanism is constructed to learn domain-invariant features from the training data of the source and target domains; then, the feature extractor and the domain discriminator are trained using an adversarial training strategy to optimize the performance of both, and the labeled classifier is trained to enable accurate fault identification; finally, the test data are fed into the model, and the labeled classifier can accurately assign the unlabeled target domain data to each category, which enables fault diagnosis under variable running requirements. Results and Conclusion The experiments in this article use the Case Western Reserve University and Laboratory self-test rolling bearing dataset and show that the presented approach can reduce the domain distribution differences, and obtain 95.43% diagnostic accuracy in case of a large difference in working conditions and 100% diagnostic accuracy in case of similar working conditions, compared with other methods, the proposed method has better transfer effect and higher diagnostic accuracy. | ||
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| 650 | 4 | |a Domain adversarial transfer network |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Inception V1 module |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Self-attention mechanism |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fault diagnosis |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Zhang, Jie |4 aut | |
| 700 | 1 | |a Li, Wanxiang |4 aut | |
| 700 | 1 | |a Qian, Shiqi |4 aut | |
| 700 | 1 | |a Gao, Maosheng |4 aut | |
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10.1007/s42417-022-00823-2 doi (DE-627)SPR054642752 (SPR)s42417-022-00823-2-e DE-627 ger DE-627 rakwb eng Shang, Zhiwu verfasserin (orcid)0000-0002-7310-0921 aut A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose Due to the influence of external factors such as noise, the operating conditions of rotating machinery in actual operation are not constant, and it is difficult to obtain high diagnostic accuracy using differently distributed training data and test data for fault diagnosis. Methods To solve the problem of fault diagnosis under variable working conditions, we come up with a fault diagnosis approach on the account of Inception V1 module with self-attention mechanism for domain adversarial transfer network (IS-DATN). First, a feature extractor based on the Inception V1 module and the self-attention mechanism is constructed to learn domain-invariant features from the training data of the source and target domains; then, the feature extractor and the domain discriminator are trained using an adversarial training strategy to optimize the performance of both, and the labeled classifier is trained to enable accurate fault identification; finally, the test data are fed into the model, and the labeled classifier can accurately assign the unlabeled target domain data to each category, which enables fault diagnosis under variable running requirements. Results and Conclusion The experiments in this article use the Case Western Reserve University and Laboratory self-test rolling bearing dataset and show that the presented approach can reduce the domain distribution differences, and obtain 95.43% diagnostic accuracy in case of a large difference in working conditions and 100% diagnostic accuracy in case of similar working conditions, compared with other methods, the proposed method has better transfer effect and higher diagnostic accuracy. Transfer learning (dpeaa)DE-He213 Domain adversarial transfer network (dpeaa)DE-He213 Inception V1 module (dpeaa)DE-He213 Self-attention mechanism (dpeaa)DE-He213 Fault diagnosis (dpeaa)DE-He213 Zhang, Jie aut Li, Wanxiang aut Qian, Shiqi aut Gao, Maosheng aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2022), 1 vom: 24. Dez., Seite 1-17 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2022 number:1 day:24 month:12 pages:1-17 https://dx.doi.org/10.1007/s42417-022-00823-2 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_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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2022 1 24 12 1-17 |
| spelling |
10.1007/s42417-022-00823-2 doi (DE-627)SPR054642752 (SPR)s42417-022-00823-2-e DE-627 ger DE-627 rakwb eng Shang, Zhiwu verfasserin (orcid)0000-0002-7310-0921 aut A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose Due to the influence of external factors such as noise, the operating conditions of rotating machinery in actual operation are not constant, and it is difficult to obtain high diagnostic accuracy using differently distributed training data and test data for fault diagnosis. Methods To solve the problem of fault diagnosis under variable working conditions, we come up with a fault diagnosis approach on the account of Inception V1 module with self-attention mechanism for domain adversarial transfer network (IS-DATN). First, a feature extractor based on the Inception V1 module and the self-attention mechanism is constructed to learn domain-invariant features from the training data of the source and target domains; then, the feature extractor and the domain discriminator are trained using an adversarial training strategy to optimize the performance of both, and the labeled classifier is trained to enable accurate fault identification; finally, the test data are fed into the model, and the labeled classifier can accurately assign the unlabeled target domain data to each category, which enables fault diagnosis under variable running requirements. Results and Conclusion The experiments in this article use the Case Western Reserve University and Laboratory self-test rolling bearing dataset and show that the presented approach can reduce the domain distribution differences, and obtain 95.43% diagnostic accuracy in case of a large difference in working conditions and 100% diagnostic accuracy in case of similar working conditions, compared with other methods, the proposed method has better transfer effect and higher diagnostic accuracy. Transfer learning (dpeaa)DE-He213 Domain adversarial transfer network (dpeaa)DE-He213 Inception V1 module (dpeaa)DE-He213 Self-attention mechanism (dpeaa)DE-He213 Fault diagnosis (dpeaa)DE-He213 Zhang, Jie aut Li, Wanxiang aut Qian, Shiqi aut Gao, Maosheng aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2022), 1 vom: 24. Dez., Seite 1-17 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2022 number:1 day:24 month:12 pages:1-17 https://dx.doi.org/10.1007/s42417-022-00823-2 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_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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2022 1 24 12 1-17 |
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10.1007/s42417-022-00823-2 doi (DE-627)SPR054642752 (SPR)s42417-022-00823-2-e DE-627 ger DE-627 rakwb eng Shang, Zhiwu verfasserin (orcid)0000-0002-7310-0921 aut A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose Due to the influence of external factors such as noise, the operating conditions of rotating machinery in actual operation are not constant, and it is difficult to obtain high diagnostic accuracy using differently distributed training data and test data for fault diagnosis. Methods To solve the problem of fault diagnosis under variable working conditions, we come up with a fault diagnosis approach on the account of Inception V1 module with self-attention mechanism for domain adversarial transfer network (IS-DATN). First, a feature extractor based on the Inception V1 module and the self-attention mechanism is constructed to learn domain-invariant features from the training data of the source and target domains; then, the feature extractor and the domain discriminator are trained using an adversarial training strategy to optimize the performance of both, and the labeled classifier is trained to enable accurate fault identification; finally, the test data are fed into the model, and the labeled classifier can accurately assign the unlabeled target domain data to each category, which enables fault diagnosis under variable running requirements. Results and Conclusion The experiments in this article use the Case Western Reserve University and Laboratory self-test rolling bearing dataset and show that the presented approach can reduce the domain distribution differences, and obtain 95.43% diagnostic accuracy in case of a large difference in working conditions and 100% diagnostic accuracy in case of similar working conditions, compared with other methods, the proposed method has better transfer effect and higher diagnostic accuracy. Transfer learning (dpeaa)DE-He213 Domain adversarial transfer network (dpeaa)DE-He213 Inception V1 module (dpeaa)DE-He213 Self-attention mechanism (dpeaa)DE-He213 Fault diagnosis (dpeaa)DE-He213 Zhang, Jie aut Li, Wanxiang aut Qian, Shiqi aut Gao, Maosheng aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2022), 1 vom: 24. Dez., Seite 1-17 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2022 number:1 day:24 month:12 pages:1-17 https://dx.doi.org/10.1007/s42417-022-00823-2 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_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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2022 1 24 12 1-17 |
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10.1007/s42417-022-00823-2 doi (DE-627)SPR054642752 (SPR)s42417-022-00823-2-e DE-627 ger DE-627 rakwb eng Shang, Zhiwu verfasserin (orcid)0000-0002-7310-0921 aut A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose Due to the influence of external factors such as noise, the operating conditions of rotating machinery in actual operation are not constant, and it is difficult to obtain high diagnostic accuracy using differently distributed training data and test data for fault diagnosis. Methods To solve the problem of fault diagnosis under variable working conditions, we come up with a fault diagnosis approach on the account of Inception V1 module with self-attention mechanism for domain adversarial transfer network (IS-DATN). First, a feature extractor based on the Inception V1 module and the self-attention mechanism is constructed to learn domain-invariant features from the training data of the source and target domains; then, the feature extractor and the domain discriminator are trained using an adversarial training strategy to optimize the performance of both, and the labeled classifier is trained to enable accurate fault identification; finally, the test data are fed into the model, and the labeled classifier can accurately assign the unlabeled target domain data to each category, which enables fault diagnosis under variable running requirements. Results and Conclusion The experiments in this article use the Case Western Reserve University and Laboratory self-test rolling bearing dataset and show that the presented approach can reduce the domain distribution differences, and obtain 95.43% diagnostic accuracy in case of a large difference in working conditions and 100% diagnostic accuracy in case of similar working conditions, compared with other methods, the proposed method has better transfer effect and higher diagnostic accuracy. Transfer learning (dpeaa)DE-He213 Domain adversarial transfer network (dpeaa)DE-He213 Inception V1 module (dpeaa)DE-He213 Self-attention mechanism (dpeaa)DE-He213 Fault diagnosis (dpeaa)DE-He213 Zhang, Jie aut Li, Wanxiang aut Qian, Shiqi aut Gao, Maosheng aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2022), 1 vom: 24. Dez., Seite 1-17 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2022 number:1 day:24 month:12 pages:1-17 https://dx.doi.org/10.1007/s42417-022-00823-2 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_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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2022 1 24 12 1-17 |
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10.1007/s42417-022-00823-2 doi (DE-627)SPR054642752 (SPR)s42417-022-00823-2-e DE-627 ger DE-627 rakwb eng Shang, Zhiwu verfasserin (orcid)0000-0002-7310-0921 aut A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose Due to the influence of external factors such as noise, the operating conditions of rotating machinery in actual operation are not constant, and it is difficult to obtain high diagnostic accuracy using differently distributed training data and test data for fault diagnosis. Methods To solve the problem of fault diagnosis under variable working conditions, we come up with a fault diagnosis approach on the account of Inception V1 module with self-attention mechanism for domain adversarial transfer network (IS-DATN). First, a feature extractor based on the Inception V1 module and the self-attention mechanism is constructed to learn domain-invariant features from the training data of the source and target domains; then, the feature extractor and the domain discriminator are trained using an adversarial training strategy to optimize the performance of both, and the labeled classifier is trained to enable accurate fault identification; finally, the test data are fed into the model, and the labeled classifier can accurately assign the unlabeled target domain data to each category, which enables fault diagnosis under variable running requirements. Results and Conclusion The experiments in this article use the Case Western Reserve University and Laboratory self-test rolling bearing dataset and show that the presented approach can reduce the domain distribution differences, and obtain 95.43% diagnostic accuracy in case of a large difference in working conditions and 100% diagnostic accuracy in case of similar working conditions, compared with other methods, the proposed method has better transfer effect and higher diagnostic accuracy. Transfer learning (dpeaa)DE-He213 Domain adversarial transfer network (dpeaa)DE-He213 Inception V1 module (dpeaa)DE-He213 Self-attention mechanism (dpeaa)DE-He213 Fault diagnosis (dpeaa)DE-He213 Zhang, Jie aut Li, Wanxiang aut Qian, Shiqi aut Gao, Maosheng aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2022), 1 vom: 24. Dez., Seite 1-17 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2022 number:1 day:24 month:12 pages:1-17 https://dx.doi.org/10.1007/s42417-022-00823-2 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_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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2022 1 24 12 1-17 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR054642752</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240204064640.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240204s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s42417-022-00823-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR054642752</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s42417-022-00823-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Shang, Zhiwu</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-7310-0921</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Krishtel eMaging Solutions Private Limited 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose Due to the influence of external factors such as noise, the operating conditions of rotating machinery in actual operation are not constant, and it is difficult to obtain high diagnostic accuracy using differently distributed training data and test data for fault diagnosis. Methods To solve the problem of fault diagnosis under variable working conditions, we come up with a fault diagnosis approach on the account of Inception V1 module with self-attention mechanism for domain adversarial transfer network (IS-DATN). First, a feature extractor based on the Inception V1 module and the self-attention mechanism is constructed to learn domain-invariant features from the training data of the source and target domains; then, the feature extractor and the domain discriminator are trained using an adversarial training strategy to optimize the performance of both, and the labeled classifier is trained to enable accurate fault identification; finally, the test data are fed into the model, and the labeled classifier can accurately assign the unlabeled target domain data to each category, which enables fault diagnosis under variable running requirements. Results and Conclusion The experiments in this article use the Case Western Reserve University and Laboratory self-test rolling bearing dataset and show that the presented approach can reduce the domain distribution differences, and obtain 95.43% diagnostic accuracy in case of a large difference in working conditions and 100% diagnostic accuracy in case of similar working conditions, compared with other methods, the proposed method has better transfer effect and higher diagnostic accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Transfer learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Domain adversarial transfer network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Inception V1 module</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Self-attention mechanism</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fault diagnosis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Jie</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Wanxiang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qian, Shiqi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Maosheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of vibration engineering & technologies</subfield><subfield code="d">Singapore : Springer Singapore, 2018</subfield><subfield code="g">12(2022), 1 vom: 24. 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Shang, Zhiwu |
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Shang, Zhiwu misc Transfer learning misc Domain adversarial transfer network misc Inception V1 module misc Self-attention mechanism misc Fault diagnosis A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions |
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A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions Transfer learning (dpeaa)DE-He213 Domain adversarial transfer network (dpeaa)DE-He213 Inception V1 module (dpeaa)DE-He213 Self-attention mechanism (dpeaa)DE-He213 Fault diagnosis (dpeaa)DE-He213 |
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domain adversarial transfer model with inception and attention network for rolling bearing fault diagnosis under variable operating conditions |
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A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions |
| abstract |
Purpose Due to the influence of external factors such as noise, the operating conditions of rotating machinery in actual operation are not constant, and it is difficult to obtain high diagnostic accuracy using differently distributed training data and test data for fault diagnosis. Methods To solve the problem of fault diagnosis under variable working conditions, we come up with a fault diagnosis approach on the account of Inception V1 module with self-attention mechanism for domain adversarial transfer network (IS-DATN). First, a feature extractor based on the Inception V1 module and the self-attention mechanism is constructed to learn domain-invariant features from the training data of the source and target domains; then, the feature extractor and the domain discriminator are trained using an adversarial training strategy to optimize the performance of both, and the labeled classifier is trained to enable accurate fault identification; finally, the test data are fed into the model, and the labeled classifier can accurately assign the unlabeled target domain data to each category, which enables fault diagnosis under variable running requirements. Results and Conclusion The experiments in this article use the Case Western Reserve University and Laboratory self-test rolling bearing dataset and show that the presented approach can reduce the domain distribution differences, and obtain 95.43% diagnostic accuracy in case of a large difference in working conditions and 100% diagnostic accuracy in case of similar working conditions, compared with other methods, the proposed method has better transfer effect and higher diagnostic accuracy. © Krishtel eMaging Solutions Private Limited 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Purpose Due to the influence of external factors such as noise, the operating conditions of rotating machinery in actual operation are not constant, and it is difficult to obtain high diagnostic accuracy using differently distributed training data and test data for fault diagnosis. Methods To solve the problem of fault diagnosis under variable working conditions, we come up with a fault diagnosis approach on the account of Inception V1 module with self-attention mechanism for domain adversarial transfer network (IS-DATN). First, a feature extractor based on the Inception V1 module and the self-attention mechanism is constructed to learn domain-invariant features from the training data of the source and target domains; then, the feature extractor and the domain discriminator are trained using an adversarial training strategy to optimize the performance of both, and the labeled classifier is trained to enable accurate fault identification; finally, the test data are fed into the model, and the labeled classifier can accurately assign the unlabeled target domain data to each category, which enables fault diagnosis under variable running requirements. Results and Conclusion The experiments in this article use the Case Western Reserve University and Laboratory self-test rolling bearing dataset and show that the presented approach can reduce the domain distribution differences, and obtain 95.43% diagnostic accuracy in case of a large difference in working conditions and 100% diagnostic accuracy in case of similar working conditions, compared with other methods, the proposed method has better transfer effect and higher diagnostic accuracy. © Krishtel eMaging Solutions Private Limited 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Purpose Due to the influence of external factors such as noise, the operating conditions of rotating machinery in actual operation are not constant, and it is difficult to obtain high diagnostic accuracy using differently distributed training data and test data for fault diagnosis. Methods To solve the problem of fault diagnosis under variable working conditions, we come up with a fault diagnosis approach on the account of Inception V1 module with self-attention mechanism for domain adversarial transfer network (IS-DATN). First, a feature extractor based on the Inception V1 module and the self-attention mechanism is constructed to learn domain-invariant features from the training data of the source and target domains; then, the feature extractor and the domain discriminator are trained using an adversarial training strategy to optimize the performance of both, and the labeled classifier is trained to enable accurate fault identification; finally, the test data are fed into the model, and the labeled classifier can accurately assign the unlabeled target domain data to each category, which enables fault diagnosis under variable running requirements. Results and Conclusion The experiments in this article use the Case Western Reserve University and Laboratory self-test rolling bearing dataset and show that the presented approach can reduce the domain distribution differences, and obtain 95.43% diagnostic accuracy in case of a large difference in working conditions and 100% diagnostic accuracy in case of similar working conditions, compared with other methods, the proposed method has better transfer effect and higher diagnostic accuracy. © Krishtel eMaging Solutions Private Limited 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions |
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https://dx.doi.org/10.1007/s42417-022-00823-2 |
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Zhang, Jie Li, Wanxiang Qian, Shiqi Gao, Maosheng |
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Zhang, Jie Li, Wanxiang Qian, Shiqi Gao, Maosheng |
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10.1007/s42417-022-00823-2 |
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2024-07-04T02:29:14.174Z |
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| score |
7.3998737 |