A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data
Abstract Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which pu...
Ausführliche Beschreibung
Gespeichert in:
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Zhao, Ke [verfasserIn] |
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Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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Enthalten in: Journal of intelligent manufacturing - Springer US, 1990, 33(2020), 1 vom: 08. Sept., Seite 151-165 |
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volume:33 ; year:2020 ; number:1 ; day:08 ; month:09 ; pages:151-165 |
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| DOI / URN: |
10.1007/s10845-020-01657-z |
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| 520 | |a Abstract Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which puts forward higher requirements on the ability of the diagnosis methods. To tackle the mentioned problem, a novel transfer learning method based on a little labeled data is proposed, which uses bidirectional gated recurrent unit (BiGRU) and Manifold Embedded Distribution Alignment (MEDA). Firstly, frequency spectrum datasets are utilized to remove the redundant information of raw vibration signals. Secondly, the BiGRU network is constructed to generate auxiliary samples that are utilized as source domain. Finally, MEDA, as the most powerful non-deep transfer learning method, is applied to align the distribution of these auxiliary samples generated by BiGRU and the unlabeled samples from target domain. Experiment results indicate the excellent performance of the proposed method under a little labeled data. | ||
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10.1007/s10845-020-01657-z doi (DE-627)OLC2077724196 (DE-He213)s10845-020-01657-z-p DE-627 ger DE-627 rakwb eng 620 004 VZ Zhao, Ke verfasserin aut A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which puts forward higher requirements on the ability of the diagnosis methods. To tackle the mentioned problem, a novel transfer learning method based on a little labeled data is proposed, which uses bidirectional gated recurrent unit (BiGRU) and Manifold Embedded Distribution Alignment (MEDA). Firstly, frequency spectrum datasets are utilized to remove the redundant information of raw vibration signals. Secondly, the BiGRU network is constructed to generate auxiliary samples that are utilized as source domain. Finally, MEDA, as the most powerful non-deep transfer learning method, is applied to align the distribution of these auxiliary samples generated by BiGRU and the unlabeled samples from target domain. Experiment results indicate the excellent performance of the proposed method under a little labeled data. Transfer learning Bidirectional gated recurrent unit Manifold Embedded Distribution Alignment Jiang, Hongkai (orcid)0000-0001-6180-4641 aut Wu, Zhenghong aut Lu, Tengfei aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 33(2020), 1 vom: 08. Sept., Seite 151-165 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:33 year:2020 number:1 day:08 month:09 pages:151-165 https://doi.org/10.1007/s10845-020-01657-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 33 2020 1 08 09 151-165 |
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10.1007/s10845-020-01657-z doi (DE-627)OLC2077724196 (DE-He213)s10845-020-01657-z-p DE-627 ger DE-627 rakwb eng 620 004 VZ Zhao, Ke verfasserin aut A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which puts forward higher requirements on the ability of the diagnosis methods. To tackle the mentioned problem, a novel transfer learning method based on a little labeled data is proposed, which uses bidirectional gated recurrent unit (BiGRU) and Manifold Embedded Distribution Alignment (MEDA). Firstly, frequency spectrum datasets are utilized to remove the redundant information of raw vibration signals. Secondly, the BiGRU network is constructed to generate auxiliary samples that are utilized as source domain. Finally, MEDA, as the most powerful non-deep transfer learning method, is applied to align the distribution of these auxiliary samples generated by BiGRU and the unlabeled samples from target domain. Experiment results indicate the excellent performance of the proposed method under a little labeled data. Transfer learning Bidirectional gated recurrent unit Manifold Embedded Distribution Alignment Jiang, Hongkai (orcid)0000-0001-6180-4641 aut Wu, Zhenghong aut Lu, Tengfei aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 33(2020), 1 vom: 08. Sept., Seite 151-165 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:33 year:2020 number:1 day:08 month:09 pages:151-165 https://doi.org/10.1007/s10845-020-01657-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 33 2020 1 08 09 151-165 |
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10.1007/s10845-020-01657-z doi (DE-627)OLC2077724196 (DE-He213)s10845-020-01657-z-p DE-627 ger DE-627 rakwb eng 620 004 VZ Zhao, Ke verfasserin aut A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which puts forward higher requirements on the ability of the diagnosis methods. To tackle the mentioned problem, a novel transfer learning method based on a little labeled data is proposed, which uses bidirectional gated recurrent unit (BiGRU) and Manifold Embedded Distribution Alignment (MEDA). Firstly, frequency spectrum datasets are utilized to remove the redundant information of raw vibration signals. Secondly, the BiGRU network is constructed to generate auxiliary samples that are utilized as source domain. Finally, MEDA, as the most powerful non-deep transfer learning method, is applied to align the distribution of these auxiliary samples generated by BiGRU and the unlabeled samples from target domain. Experiment results indicate the excellent performance of the proposed method under a little labeled data. Transfer learning Bidirectional gated recurrent unit Manifold Embedded Distribution Alignment Jiang, Hongkai (orcid)0000-0001-6180-4641 aut Wu, Zhenghong aut Lu, Tengfei aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 33(2020), 1 vom: 08. Sept., Seite 151-165 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:33 year:2020 number:1 day:08 month:09 pages:151-165 https://doi.org/10.1007/s10845-020-01657-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 33 2020 1 08 09 151-165 |
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10.1007/s10845-020-01657-z doi (DE-627)OLC2077724196 (DE-He213)s10845-020-01657-z-p DE-627 ger DE-627 rakwb eng 620 004 VZ Zhao, Ke verfasserin aut A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which puts forward higher requirements on the ability of the diagnosis methods. To tackle the mentioned problem, a novel transfer learning method based on a little labeled data is proposed, which uses bidirectional gated recurrent unit (BiGRU) and Manifold Embedded Distribution Alignment (MEDA). Firstly, frequency spectrum datasets are utilized to remove the redundant information of raw vibration signals. Secondly, the BiGRU network is constructed to generate auxiliary samples that are utilized as source domain. Finally, MEDA, as the most powerful non-deep transfer learning method, is applied to align the distribution of these auxiliary samples generated by BiGRU and the unlabeled samples from target domain. Experiment results indicate the excellent performance of the proposed method under a little labeled data. Transfer learning Bidirectional gated recurrent unit Manifold Embedded Distribution Alignment Jiang, Hongkai (orcid)0000-0001-6180-4641 aut Wu, Zhenghong aut Lu, Tengfei aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 33(2020), 1 vom: 08. Sept., Seite 151-165 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:33 year:2020 number:1 day:08 month:09 pages:151-165 https://doi.org/10.1007/s10845-020-01657-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 33 2020 1 08 09 151-165 |
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Abstract Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which puts forward higher requirements on the ability of the diagnosis methods. To tackle the mentioned problem, a novel transfer learning method based on a little labeled data is proposed, which uses bidirectional gated recurrent unit (BiGRU) and Manifold Embedded Distribution Alignment (MEDA). Firstly, frequency spectrum datasets are utilized to remove the redundant information of raw vibration signals. Secondly, the BiGRU network is constructed to generate auxiliary samples that are utilized as source domain. Finally, MEDA, as the most powerful non-deep transfer learning method, is applied to align the distribution of these auxiliary samples generated by BiGRU and the unlabeled samples from target domain. Experiment results indicate the excellent performance of the proposed method under a little labeled data. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstractGer |
Abstract Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which puts forward higher requirements on the ability of the diagnosis methods. To tackle the mentioned problem, a novel transfer learning method based on a little labeled data is proposed, which uses bidirectional gated recurrent unit (BiGRU) and Manifold Embedded Distribution Alignment (MEDA). Firstly, frequency spectrum datasets are utilized to remove the redundant information of raw vibration signals. Secondly, the BiGRU network is constructed to generate auxiliary samples that are utilized as source domain. Finally, MEDA, as the most powerful non-deep transfer learning method, is applied to align the distribution of these auxiliary samples generated by BiGRU and the unlabeled samples from target domain. Experiment results indicate the excellent performance of the proposed method under a little labeled data. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which puts forward higher requirements on the ability of the diagnosis methods. To tackle the mentioned problem, a novel transfer learning method based on a little labeled data is proposed, which uses bidirectional gated recurrent unit (BiGRU) and Manifold Embedded Distribution Alignment (MEDA). Firstly, frequency spectrum datasets are utilized to remove the redundant information of raw vibration signals. Secondly, the BiGRU network is constructed to generate auxiliary samples that are utilized as source domain. Finally, MEDA, as the most powerful non-deep transfer learning method, is applied to align the distribution of these auxiliary samples generated by BiGRU and the unlabeled samples from target domain. Experiment results indicate the excellent performance of the proposed method under a little labeled data. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data |
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Jiang, Hongkai Wu, Zhenghong Lu, Tengfei |
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