Intelligent Fault Diagnosis of Rolling Bearing Using Adaptive Deep Gated Recurrent Unit
Abstract Rolling bearing plays a significant part in enhancing the reliability and security of locomotive. Therefore, how to accurately and automatically identify the rolling bearing faults is becoming more and more urgent. For this purpose, an adaptive rolling bearing fault diagnosis method is prop...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhao, Ke [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
Rolling bearing fault diagnosis |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Springer US, 1994, 51(2019), 2 vom: 25. Okt., Seite 1165-1184 |
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| Übergeordnetes Werk: |
volume:51 ; year:2019 ; number:2 ; day:25 ; month:10 ; pages:1165-1184 |
| Links: |
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| DOI / URN: |
10.1007/s11063-019-10137-2 |
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OLC2044716100 |
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| 520 | |a Abstract Rolling bearing plays a significant part in enhancing the reliability and security of locomotive. Therefore, how to accurately and automatically identify the rolling bearing faults is becoming more and more urgent. For this purpose, an adaptive rolling bearing fault diagnosis method is proposed in this paper. Firstly, deep gated recurrent unit is constructed to effectively learn the features of bearing vibration signals. Secondly, artificial fish swarm algorithm is applied to obtain the key parameters of deep gated recurrent unit. Finally, extreme learning machine is used to accurately classify the learned features and provide final diagnosis result. The proposed method is verified by the measured locomotive bearing vibration signals and the results indicate the feature learning ability of deep gated recurrent unit is powerful and the proposed method achieves more accurate and robust performance than other diagnosis methods. | ||
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10.1007/s11063-019-10137-2 doi (DE-627)OLC2044716100 (DE-He213)s11063-019-10137-2-p DE-627 ger DE-627 rakwb eng 000 VZ Zhao, Ke verfasserin aut Intelligent Fault Diagnosis of Rolling Bearing Using Adaptive Deep Gated Recurrent Unit 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Rolling bearing plays a significant part in enhancing the reliability and security of locomotive. Therefore, how to accurately and automatically identify the rolling bearing faults is becoming more and more urgent. For this purpose, an adaptive rolling bearing fault diagnosis method is proposed in this paper. Firstly, deep gated recurrent unit is constructed to effectively learn the features of bearing vibration signals. Secondly, artificial fish swarm algorithm is applied to obtain the key parameters of deep gated recurrent unit. Finally, extreme learning machine is used to accurately classify the learned features and provide final diagnosis result. The proposed method is verified by the measured locomotive bearing vibration signals and the results indicate the feature learning ability of deep gated recurrent unit is powerful and the proposed method achieves more accurate and robust performance than other diagnosis methods. Rolling bearing fault diagnosis Deep gated recurrent unit Artificial fish swarm algorithm Extreme learning machine Feature learning ability Shao, Haidong aut Enthalten in Neural processing letters Springer US, 1994 51(2019), 2 vom: 25. Okt., Seite 1165-1184 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:51 year:2019 number:2 day:25 month:10 pages:1165-1184 https://doi.org/10.1007/s11063-019-10137-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 51 2019 2 25 10 1165-1184 |
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10.1007/s11063-019-10137-2 doi (DE-627)OLC2044716100 (DE-He213)s11063-019-10137-2-p DE-627 ger DE-627 rakwb eng 000 VZ Zhao, Ke verfasserin aut Intelligent Fault Diagnosis of Rolling Bearing Using Adaptive Deep Gated Recurrent Unit 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Rolling bearing plays a significant part in enhancing the reliability and security of locomotive. Therefore, how to accurately and automatically identify the rolling bearing faults is becoming more and more urgent. For this purpose, an adaptive rolling bearing fault diagnosis method is proposed in this paper. Firstly, deep gated recurrent unit is constructed to effectively learn the features of bearing vibration signals. Secondly, artificial fish swarm algorithm is applied to obtain the key parameters of deep gated recurrent unit. Finally, extreme learning machine is used to accurately classify the learned features and provide final diagnosis result. The proposed method is verified by the measured locomotive bearing vibration signals and the results indicate the feature learning ability of deep gated recurrent unit is powerful and the proposed method achieves more accurate and robust performance than other diagnosis methods. Rolling bearing fault diagnosis Deep gated recurrent unit Artificial fish swarm algorithm Extreme learning machine Feature learning ability Shao, Haidong aut Enthalten in Neural processing letters Springer US, 1994 51(2019), 2 vom: 25. Okt., Seite 1165-1184 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:51 year:2019 number:2 day:25 month:10 pages:1165-1184 https://doi.org/10.1007/s11063-019-10137-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 51 2019 2 25 10 1165-1184 |
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10.1007/s11063-019-10137-2 doi (DE-627)OLC2044716100 (DE-He213)s11063-019-10137-2-p DE-627 ger DE-627 rakwb eng 000 VZ Zhao, Ke verfasserin aut Intelligent Fault Diagnosis of Rolling Bearing Using Adaptive Deep Gated Recurrent Unit 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Rolling bearing plays a significant part in enhancing the reliability and security of locomotive. Therefore, how to accurately and automatically identify the rolling bearing faults is becoming more and more urgent. For this purpose, an adaptive rolling bearing fault diagnosis method is proposed in this paper. Firstly, deep gated recurrent unit is constructed to effectively learn the features of bearing vibration signals. Secondly, artificial fish swarm algorithm is applied to obtain the key parameters of deep gated recurrent unit. Finally, extreme learning machine is used to accurately classify the learned features and provide final diagnosis result. The proposed method is verified by the measured locomotive bearing vibration signals and the results indicate the feature learning ability of deep gated recurrent unit is powerful and the proposed method achieves more accurate and robust performance than other diagnosis methods. Rolling bearing fault diagnosis Deep gated recurrent unit Artificial fish swarm algorithm Extreme learning machine Feature learning ability Shao, Haidong aut Enthalten in Neural processing letters Springer US, 1994 51(2019), 2 vom: 25. Okt., Seite 1165-1184 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:51 year:2019 number:2 day:25 month:10 pages:1165-1184 https://doi.org/10.1007/s11063-019-10137-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 51 2019 2 25 10 1165-1184 |
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10.1007/s11063-019-10137-2 doi (DE-627)OLC2044716100 (DE-He213)s11063-019-10137-2-p DE-627 ger DE-627 rakwb eng 000 VZ Zhao, Ke verfasserin aut Intelligent Fault Diagnosis of Rolling Bearing Using Adaptive Deep Gated Recurrent Unit 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Rolling bearing plays a significant part in enhancing the reliability and security of locomotive. Therefore, how to accurately and automatically identify the rolling bearing faults is becoming more and more urgent. For this purpose, an adaptive rolling bearing fault diagnosis method is proposed in this paper. Firstly, deep gated recurrent unit is constructed to effectively learn the features of bearing vibration signals. Secondly, artificial fish swarm algorithm is applied to obtain the key parameters of deep gated recurrent unit. Finally, extreme learning machine is used to accurately classify the learned features and provide final diagnosis result. The proposed method is verified by the measured locomotive bearing vibration signals and the results indicate the feature learning ability of deep gated recurrent unit is powerful and the proposed method achieves more accurate and robust performance than other diagnosis methods. Rolling bearing fault diagnosis Deep gated recurrent unit Artificial fish swarm algorithm Extreme learning machine Feature learning ability Shao, Haidong aut Enthalten in Neural processing letters Springer US, 1994 51(2019), 2 vom: 25. Okt., Seite 1165-1184 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:51 year:2019 number:2 day:25 month:10 pages:1165-1184 https://doi.org/10.1007/s11063-019-10137-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 51 2019 2 25 10 1165-1184 |
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Abstract Rolling bearing plays a significant part in enhancing the reliability and security of locomotive. Therefore, how to accurately and automatically identify the rolling bearing faults is becoming more and more urgent. For this purpose, an adaptive rolling bearing fault diagnosis method is proposed in this paper. Firstly, deep gated recurrent unit is constructed to effectively learn the features of bearing vibration signals. Secondly, artificial fish swarm algorithm is applied to obtain the key parameters of deep gated recurrent unit. Finally, extreme learning machine is used to accurately classify the learned features and provide final diagnosis result. The proposed method is verified by the measured locomotive bearing vibration signals and the results indicate the feature learning ability of deep gated recurrent unit is powerful and the proposed method achieves more accurate and robust performance than other diagnosis methods. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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Abstract Rolling bearing plays a significant part in enhancing the reliability and security of locomotive. Therefore, how to accurately and automatically identify the rolling bearing faults is becoming more and more urgent. For this purpose, an adaptive rolling bearing fault diagnosis method is proposed in this paper. Firstly, deep gated recurrent unit is constructed to effectively learn the features of bearing vibration signals. Secondly, artificial fish swarm algorithm is applied to obtain the key parameters of deep gated recurrent unit. Finally, extreme learning machine is used to accurately classify the learned features and provide final diagnosis result. The proposed method is verified by the measured locomotive bearing vibration signals and the results indicate the feature learning ability of deep gated recurrent unit is powerful and the proposed method achieves more accurate and robust performance than other diagnosis methods. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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Abstract Rolling bearing plays a significant part in enhancing the reliability and security of locomotive. Therefore, how to accurately and automatically identify the rolling bearing faults is becoming more and more urgent. For this purpose, an adaptive rolling bearing fault diagnosis method is proposed in this paper. Firstly, deep gated recurrent unit is constructed to effectively learn the features of bearing vibration signals. Secondly, artificial fish swarm algorithm is applied to obtain the key parameters of deep gated recurrent unit. Finally, extreme learning machine is used to accurately classify the learned features and provide final diagnosis result. The proposed method is verified by the measured locomotive bearing vibration signals and the results indicate the feature learning ability of deep gated recurrent unit is powerful and the proposed method achieves more accurate and robust performance than other diagnosis methods. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2044716100</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504133737.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2019 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11063-019-10137-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2044716100</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11063-019-10137-2-p</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="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhao, Ke</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Intelligent Fault Diagnosis of Rolling Bearing Using Adaptive Deep Gated Recurrent Unit</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Rolling bearing plays a significant part in enhancing the reliability and security of locomotive. Therefore, how to accurately and automatically identify the rolling bearing faults is becoming more and more urgent. For this purpose, an adaptive rolling bearing fault diagnosis method is proposed in this paper. Firstly, deep gated recurrent unit is constructed to effectively learn the features of bearing vibration signals. Secondly, artificial fish swarm algorithm is applied to obtain the key parameters of deep gated recurrent unit. Finally, extreme learning machine is used to accurately classify the learned features and provide final diagnosis result. 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