Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data

Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome the difficulties in this field. In most engineering scenarios, machines perform in normal con...
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Autor*in:	Xia Zong [verfasserIn] Rui Yang [verfasserIn] Hongshu Wang [verfasserIn] Minghao Du [verfasserIn] Pengfei You [verfasserIn] Su Wang [verfasserIn] Hao Su [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	fault diagnosis imbalanced data semi-supervised learning transfer learning uncertainty-aware pseudo-label selection

Übergeordnetes Werk:	In: Machines - MDPI AG, 2013, 10(2022), 7, p 515
Übergeordnetes Werk:	volume:10 ; year:2022 ; number:7, p 515

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/machines10070515

Katalog-ID:	DOAJ035938021

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spelling	10.3390/machines10070515 doi (DE-627)DOAJ035938021 (DE-599)DOAJbd80b4f8d0e745fd87199b7755376ab7 DE-627 ger DE-627 rakwb eng TJ1-1570 Xia Zong verfasserin aut Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome the difficulties in this field. In most engineering scenarios, machines perform in normal conditions, which implies that fault data may be hard to acquire and limited. Therefore, the data imbalance and the deficiency of labels are practical challenges in the fault diagnosis of machinery bearings. Among the mainstream methods, transfer learning-based fault diagnosis is highly effective, as it transfers the results of previous studies and integrates existing resources. The knowledge from the source domain is transferred via Domain Adversarial Training of Neural Networks (DANN) while the dataset of the target domain is partially labeled. A semi-supervised framework based on uncertainty-aware pseudo-label selection (UPS) is adopted in parallel to improve the model performance by utilizing abundant unlabeled data. Through experiments on two bearing datasets, the accuracy of bearing fault classification surpassed the independent approaches. fault diagnosis imbalanced data semi-supervised learning transfer learning uncertainty-aware pseudo-label selection Mechanical engineering and machinery Rui Yang verfasserin aut Hongshu Wang verfasserin aut Minghao Du verfasserin aut Pengfei You verfasserin aut Su Wang verfasserin aut Hao Su verfasserin aut In Machines MDPI AG, 2013 10(2022), 7, p 515 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:10 year:2022 number:7, p 515 https://doi.org/10.3390/machines10070515 kostenfrei https://doaj.org/article/bd80b4f8d0e745fd87199b7755376ab7 kostenfrei https://www.mdpi.com/2075-1702/10/7/515 kostenfrei https://doaj.org/toc/2075-1702 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 7, p 515
allfields_unstemmed	10.3390/machines10070515 doi (DE-627)DOAJ035938021 (DE-599)DOAJbd80b4f8d0e745fd87199b7755376ab7 DE-627 ger DE-627 rakwb eng TJ1-1570 Xia Zong verfasserin aut Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome the difficulties in this field. In most engineering scenarios, machines perform in normal conditions, which implies that fault data may be hard to acquire and limited. Therefore, the data imbalance and the deficiency of labels are practical challenges in the fault diagnosis of machinery bearings. Among the mainstream methods, transfer learning-based fault diagnosis is highly effective, as it transfers the results of previous studies and integrates existing resources. The knowledge from the source domain is transferred via Domain Adversarial Training of Neural Networks (DANN) while the dataset of the target domain is partially labeled. A semi-supervised framework based on uncertainty-aware pseudo-label selection (UPS) is adopted in parallel to improve the model performance by utilizing abundant unlabeled data. Through experiments on two bearing datasets, the accuracy of bearing fault classification surpassed the independent approaches. fault diagnosis imbalanced data semi-supervised learning transfer learning uncertainty-aware pseudo-label selection Mechanical engineering and machinery Rui Yang verfasserin aut Hongshu Wang verfasserin aut Minghao Du verfasserin aut Pengfei You verfasserin aut Su Wang verfasserin aut Hao Su verfasserin aut In Machines MDPI AG, 2013 10(2022), 7, p 515 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:10 year:2022 number:7, p 515 https://doi.org/10.3390/machines10070515 kostenfrei https://doaj.org/article/bd80b4f8d0e745fd87199b7755376ab7 kostenfrei https://www.mdpi.com/2075-1702/10/7/515 kostenfrei https://doaj.org/toc/2075-1702 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 7, p 515
allfieldsGer	10.3390/machines10070515 doi (DE-627)DOAJ035938021 (DE-599)DOAJbd80b4f8d0e745fd87199b7755376ab7 DE-627 ger DE-627 rakwb eng TJ1-1570 Xia Zong verfasserin aut Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome the difficulties in this field. In most engineering scenarios, machines perform in normal conditions, which implies that fault data may be hard to acquire and limited. Therefore, the data imbalance and the deficiency of labels are practical challenges in the fault diagnosis of machinery bearings. Among the mainstream methods, transfer learning-based fault diagnosis is highly effective, as it transfers the results of previous studies and integrates existing resources. The knowledge from the source domain is transferred via Domain Adversarial Training of Neural Networks (DANN) while the dataset of the target domain is partially labeled. A semi-supervised framework based on uncertainty-aware pseudo-label selection (UPS) is adopted in parallel to improve the model performance by utilizing abundant unlabeled data. Through experiments on two bearing datasets, the accuracy of bearing fault classification surpassed the independent approaches. fault diagnosis imbalanced data semi-supervised learning transfer learning uncertainty-aware pseudo-label selection Mechanical engineering and machinery Rui Yang verfasserin aut Hongshu Wang verfasserin aut Minghao Du verfasserin aut Pengfei You verfasserin aut Su Wang verfasserin aut Hao Su verfasserin aut In Machines MDPI AG, 2013 10(2022), 7, p 515 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:10 year:2022 number:7, p 515 https://doi.org/10.3390/machines10070515 kostenfrei https://doaj.org/article/bd80b4f8d0e745fd87199b7755376ab7 kostenfrei https://www.mdpi.com/2075-1702/10/7/515 kostenfrei https://doaj.org/toc/2075-1702 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 7, p 515
allfieldsSound	10.3390/machines10070515 doi (DE-627)DOAJ035938021 (DE-599)DOAJbd80b4f8d0e745fd87199b7755376ab7 DE-627 ger DE-627 rakwb eng TJ1-1570 Xia Zong verfasserin aut Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome the difficulties in this field. In most engineering scenarios, machines perform in normal conditions, which implies that fault data may be hard to acquire and limited. Therefore, the data imbalance and the deficiency of labels are practical challenges in the fault diagnosis of machinery bearings. Among the mainstream methods, transfer learning-based fault diagnosis is highly effective, as it transfers the results of previous studies and integrates existing resources. The knowledge from the source domain is transferred via Domain Adversarial Training of Neural Networks (DANN) while the dataset of the target domain is partially labeled. A semi-supervised framework based on uncertainty-aware pseudo-label selection (UPS) is adopted in parallel to improve the model performance by utilizing abundant unlabeled data. Through experiments on two bearing datasets, the accuracy of bearing fault classification surpassed the independent approaches. fault diagnosis imbalanced data semi-supervised learning transfer learning uncertainty-aware pseudo-label selection Mechanical engineering and machinery Rui Yang verfasserin aut Hongshu Wang verfasserin aut Minghao Du verfasserin aut Pengfei You verfasserin aut Su Wang verfasserin aut Hao Su verfasserin aut In Machines MDPI AG, 2013 10(2022), 7, p 515 (DE-627)73728823X (DE-600)2704328-9 20751702 nnns volume:10 year:2022 number:7, p 515 https://doi.org/10.3390/machines10070515 kostenfrei https://doaj.org/article/bd80b4f8d0e745fd87199b7755376ab7 kostenfrei https://www.mdpi.com/2075-1702/10/7/515 kostenfrei https://doaj.org/toc/2075-1702 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 7, p 515
language	English
source	In Machines 10(2022), 7, p 515 volume:10 year:2022 number:7, p 515
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topic_facet	fault diagnosis imbalanced data semi-supervised learning transfer learning uncertainty-aware pseudo-label selection Mechanical engineering and machinery
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authorswithroles_txt_mv	Xia Zong @@aut@@ Rui Yang @@aut@@ Hongshu Wang @@aut@@ Minghao Du @@aut@@ Pengfei You @@aut@@ Su Wang @@aut@@ Hao Su @@aut@@
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callnumber-first	T - Technology
author	Xia Zong
spellingShingle	Xia Zong misc TJ1-1570 misc fault diagnosis misc imbalanced data misc semi-supervised learning misc transfer learning misc uncertainty-aware pseudo-label selection misc Mechanical engineering and machinery Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data
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topic_title	TJ1-1570 Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data fault diagnosis imbalanced data semi-supervised learning transfer learning uncertainty-aware pseudo-label selection
topic	misc TJ1-1570 misc fault diagnosis misc imbalanced data misc semi-supervised learning misc transfer learning misc uncertainty-aware pseudo-label selection misc Mechanical engineering and machinery
topic_unstemmed	misc TJ1-1570 misc fault diagnosis misc imbalanced data misc semi-supervised learning misc transfer learning misc uncertainty-aware pseudo-label selection misc Mechanical engineering and machinery
topic_browse	misc TJ1-1570 misc fault diagnosis misc imbalanced data misc semi-supervised learning misc transfer learning misc uncertainty-aware pseudo-label selection misc Mechanical engineering and machinery
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title	Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data
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title_full	Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data
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title_sort	semi-supervised transfer learning method for bearing fault diagnosis with imbalanced data
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title_auth	Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data
abstract	Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome the difficulties in this field. In most engineering scenarios, machines perform in normal conditions, which implies that fault data may be hard to acquire and limited. Therefore, the data imbalance and the deficiency of labels are practical challenges in the fault diagnosis of machinery bearings. Among the mainstream methods, transfer learning-based fault diagnosis is highly effective, as it transfers the results of previous studies and integrates existing resources. The knowledge from the source domain is transferred via Domain Adversarial Training of Neural Networks (DANN) while the dataset of the target domain is partially labeled. A semi-supervised framework based on uncertainty-aware pseudo-label selection (UPS) is adopted in parallel to improve the model performance by utilizing abundant unlabeled data. Through experiments on two bearing datasets, the accuracy of bearing fault classification surpassed the independent approaches.
abstractGer	Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome the difficulties in this field. In most engineering scenarios, machines perform in normal conditions, which implies that fault data may be hard to acquire and limited. Therefore, the data imbalance and the deficiency of labels are practical challenges in the fault diagnosis of machinery bearings. Among the mainstream methods, transfer learning-based fault diagnosis is highly effective, as it transfers the results of previous studies and integrates existing resources. The knowledge from the source domain is transferred via Domain Adversarial Training of Neural Networks (DANN) while the dataset of the target domain is partially labeled. A semi-supervised framework based on uncertainty-aware pseudo-label selection (UPS) is adopted in parallel to improve the model performance by utilizing abundant unlabeled data. Through experiments on two bearing datasets, the accuracy of bearing fault classification surpassed the independent approaches.
abstract_unstemmed	Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome the difficulties in this field. In most engineering scenarios, machines perform in normal conditions, which implies that fault data may be hard to acquire and limited. Therefore, the data imbalance and the deficiency of labels are practical challenges in the fault diagnosis of machinery bearings. Among the mainstream methods, transfer learning-based fault diagnosis is highly effective, as it transfers the results of previous studies and integrates existing resources. The knowledge from the source domain is transferred via Domain Adversarial Training of Neural Networks (DANN) while the dataset of the target domain is partially labeled. A semi-supervised framework based on uncertainty-aware pseudo-label selection (UPS) is adopted in parallel to improve the model performance by utilizing abundant unlabeled data. Through experiments on two bearing datasets, the accuracy of bearing fault classification surpassed the independent approaches.
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title_short	Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data
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