A cross-domain intelligent fault diagnosis method based on feature transfer with improved Inception ResNet for rolling bearings under varying working condition

With the popularity of smart manufacturing, data-driven fault diagnosis methods for rolling bearings have been extensively studied in recent years. Existing rolling bearing fault diagnosis method has problems such as low precision and poor generalization ability when diagnosing multi-working conditi...
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Autor*in:	Jiaqi TIAN [verfasserIn] Bin GU [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	fault diagnosis deep learning convolution neural network transfer learning residual network

Übergeordnetes Werk:	In: Journal of Advanced Mechanical Design, Systems, and Manufacturing - The Japan Society of Mechanical Engineers, 2022, 18(2024), 2, Seite JAMDSM0012-JAMDSM0012
Übergeordnetes Werk:	volume:18 ; year:2024 ; number:2 ; pages:JAMDSM0012-JAMDSM0012

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1299/jamdsm.2024jamdsm0012

Katalog-ID:	DOAJ092477267

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allfieldsGer	10.1299/jamdsm.2024jamdsm0012 doi (DE-627)DOAJ092477267 (DE-599)DOAJc5735db7c3ae42b08fd028b36020dfb3 DE-627 ger DE-627 rakwb eng TA213-215 TJ1-1570 Jiaqi TIAN verfasserin aut A cross-domain intelligent fault diagnosis method based on feature transfer with improved Inception ResNet for rolling bearings under varying working condition 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the popularity of smart manufacturing, data-driven fault diagnosis methods for rolling bearings have been extensively studied in recent years. Existing rolling bearing fault diagnosis method has problems such as low precision and poor generalization ability when diagnosing multi-working condition bearings. In actual industrial scenarios, bearings usually operate under different operating conditions, causing differences in the probability distribution of the vibration data. Considering existing problem, this article proposes a diagnostic method of Inception ResNet Network (TL-IResnet) based on feature transfer learning. First, we utilize the Inception network to derive multiple scales of features from the original vibration signal. This enhances the capacity for feature expression in the model, and addresses the over-fitting issue in the deep model. Then the residual network is used to carry out deep learning on the fused multi-scale features to improve the residual network's ability to pay attention to important information, the self-attention mechanism is integrated into the residual network, and a new residual network structure is proposed. Finally, the maximum mean difference (MMD) is employed in output layer to measure the degree to which the probability distribution differs between the source and target domains to enhance the ability of model to transfer knowledge and complete the task of diagnosing the bearing of a machine. TL-IResnet is evaluated using the bearing dataset from Case Western Reserve University (CWRU) and the gearbox dataset from Southeast University. Experimental results demonstrate that TL-IResnet has a strong capacity to generalize information in addition to a high degree of accuracy under different conditions of operation, and has certain advantages over existing fault diagnosis methods. fault diagnosis deep learning convolution neural network transfer learning residual network Engineering machinery, tools, and implements Mechanical engineering and machinery Bin GU verfasserin aut In Journal of Advanced Mechanical Design, Systems, and Manufacturing The Japan Society of Mechanical Engineers, 2022 18(2024), 2, Seite JAMDSM0012-JAMDSM0012 (DE-627)549634266 (DE-600)2395570-3 18813054 nnns volume:18 year:2024 number:2 pages:JAMDSM0012-JAMDSM0012 https://doi.org/10.1299/jamdsm.2024jamdsm0012 kostenfrei https://doaj.org/article/c5735db7c3ae42b08fd028b36020dfb3 kostenfrei https://www.jstage.jst.go.jp/article/jamdsm/18/2/18_2024jamdsm0012/_pdf/-char/en kostenfrei https://doaj.org/toc/1881-3054 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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 18 2024 2 JAMDSM0012-JAMDSM0012
allfieldsSound	10.1299/jamdsm.2024jamdsm0012 doi (DE-627)DOAJ092477267 (DE-599)DOAJc5735db7c3ae42b08fd028b36020dfb3 DE-627 ger DE-627 rakwb eng TA213-215 TJ1-1570 Jiaqi TIAN verfasserin aut A cross-domain intelligent fault diagnosis method based on feature transfer with improved Inception ResNet for rolling bearings under varying working condition 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the popularity of smart manufacturing, data-driven fault diagnosis methods for rolling bearings have been extensively studied in recent years. Existing rolling bearing fault diagnosis method has problems such as low precision and poor generalization ability when diagnosing multi-working condition bearings. In actual industrial scenarios, bearings usually operate under different operating conditions, causing differences in the probability distribution of the vibration data. Considering existing problem, this article proposes a diagnostic method of Inception ResNet Network (TL-IResnet) based on feature transfer learning. First, we utilize the Inception network to derive multiple scales of features from the original vibration signal. This enhances the capacity for feature expression in the model, and addresses the over-fitting issue in the deep model. Then the residual network is used to carry out deep learning on the fused multi-scale features to improve the residual network's ability to pay attention to important information, the self-attention mechanism is integrated into the residual network, and a new residual network structure is proposed. Finally, the maximum mean difference (MMD) is employed in output layer to measure the degree to which the probability distribution differs between the source and target domains to enhance the ability of model to transfer knowledge and complete the task of diagnosing the bearing of a machine. TL-IResnet is evaluated using the bearing dataset from Case Western Reserve University (CWRU) and the gearbox dataset from Southeast University. Experimental results demonstrate that TL-IResnet has a strong capacity to generalize information in addition to a high degree of accuracy under different conditions of operation, and has certain advantages over existing fault diagnosis methods. fault diagnosis deep learning convolution neural network transfer learning residual network Engineering machinery, tools, and implements Mechanical engineering and machinery Bin GU verfasserin aut In Journal of Advanced Mechanical Design, Systems, and Manufacturing The Japan Society of Mechanical Engineers, 2022 18(2024), 2, Seite JAMDSM0012-JAMDSM0012 (DE-627)549634266 (DE-600)2395570-3 18813054 nnns volume:18 year:2024 number:2 pages:JAMDSM0012-JAMDSM0012 https://doi.org/10.1299/jamdsm.2024jamdsm0012 kostenfrei https://doaj.org/article/c5735db7c3ae42b08fd028b36020dfb3 kostenfrei https://www.jstage.jst.go.jp/article/jamdsm/18/2/18_2024jamdsm0012/_pdf/-char/en kostenfrei https://doaj.org/toc/1881-3054 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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 18 2024 2 JAMDSM0012-JAMDSM0012
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callnumber-first	T - Technology
author	Jiaqi TIAN
spellingShingle	Jiaqi TIAN misc TA213-215 misc TJ1-1570 misc fault diagnosis misc deep learning misc convolution neural network misc transfer learning misc residual network misc Engineering machinery, tools, and implements misc Mechanical engineering and machinery A cross-domain intelligent fault diagnosis method based on feature transfer with improved Inception ResNet for rolling bearings under varying working condition
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topic_title	TA213-215 TJ1-1570 A cross-domain intelligent fault diagnosis method based on feature transfer with improved Inception ResNet for rolling bearings under varying working condition fault diagnosis deep learning convolution neural network transfer learning residual network
topic	misc TA213-215 misc TJ1-1570 misc fault diagnosis misc deep learning misc convolution neural network misc transfer learning misc residual network misc Engineering machinery, tools, and implements misc Mechanical engineering and machinery
topic_unstemmed	misc TA213-215 misc TJ1-1570 misc fault diagnosis misc deep learning misc convolution neural network misc transfer learning misc residual network misc Engineering machinery, tools, and implements misc Mechanical engineering and machinery
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title	A cross-domain intelligent fault diagnosis method based on feature transfer with improved Inception ResNet for rolling bearings under varying working condition
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title_full	A cross-domain intelligent fault diagnosis method based on feature transfer with improved Inception ResNet for rolling bearings under varying working condition
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title_sort	cross-domain intelligent fault diagnosis method based on feature transfer with improved inception resnet for rolling bearings under varying working condition
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title_auth	A cross-domain intelligent fault diagnosis method based on feature transfer with improved Inception ResNet for rolling bearings under varying working condition
abstract	With the popularity of smart manufacturing, data-driven fault diagnosis methods for rolling bearings have been extensively studied in recent years. Existing rolling bearing fault diagnosis method has problems such as low precision and poor generalization ability when diagnosing multi-working condition bearings. In actual industrial scenarios, bearings usually operate under different operating conditions, causing differences in the probability distribution of the vibration data. Considering existing problem, this article proposes a diagnostic method of Inception ResNet Network (TL-IResnet) based on feature transfer learning. First, we utilize the Inception network to derive multiple scales of features from the original vibration signal. This enhances the capacity for feature expression in the model, and addresses the over-fitting issue in the deep model. Then the residual network is used to carry out deep learning on the fused multi-scale features to improve the residual network's ability to pay attention to important information, the self-attention mechanism is integrated into the residual network, and a new residual network structure is proposed. Finally, the maximum mean difference (MMD) is employed in output layer to measure the degree to which the probability distribution differs between the source and target domains to enhance the ability of model to transfer knowledge and complete the task of diagnosing the bearing of a machine. TL-IResnet is evaluated using the bearing dataset from Case Western Reserve University (CWRU) and the gearbox dataset from Southeast University. Experimental results demonstrate that TL-IResnet has a strong capacity to generalize information in addition to a high degree of accuracy under different conditions of operation, and has certain advantages over existing fault diagnosis methods.
abstractGer	With the popularity of smart manufacturing, data-driven fault diagnosis methods for rolling bearings have been extensively studied in recent years. Existing rolling bearing fault diagnosis method has problems such as low precision and poor generalization ability when diagnosing multi-working condition bearings. In actual industrial scenarios, bearings usually operate under different operating conditions, causing differences in the probability distribution of the vibration data. Considering existing problem, this article proposes a diagnostic method of Inception ResNet Network (TL-IResnet) based on feature transfer learning. First, we utilize the Inception network to derive multiple scales of features from the original vibration signal. This enhances the capacity for feature expression in the model, and addresses the over-fitting issue in the deep model. Then the residual network is used to carry out deep learning on the fused multi-scale features to improve the residual network's ability to pay attention to important information, the self-attention mechanism is integrated into the residual network, and a new residual network structure is proposed. Finally, the maximum mean difference (MMD) is employed in output layer to measure the degree to which the probability distribution differs between the source and target domains to enhance the ability of model to transfer knowledge and complete the task of diagnosing the bearing of a machine. TL-IResnet is evaluated using the bearing dataset from Case Western Reserve University (CWRU) and the gearbox dataset from Southeast University. Experimental results demonstrate that TL-IResnet has a strong capacity to generalize information in addition to a high degree of accuracy under different conditions of operation, and has certain advantages over existing fault diagnosis methods.
abstract_unstemmed	With the popularity of smart manufacturing, data-driven fault diagnosis methods for rolling bearings have been extensively studied in recent years. Existing rolling bearing fault diagnosis method has problems such as low precision and poor generalization ability when diagnosing multi-working condition bearings. In actual industrial scenarios, bearings usually operate under different operating conditions, causing differences in the probability distribution of the vibration data. Considering existing problem, this article proposes a diagnostic method of Inception ResNet Network (TL-IResnet) based on feature transfer learning. First, we utilize the Inception network to derive multiple scales of features from the original vibration signal. This enhances the capacity for feature expression in the model, and addresses the over-fitting issue in the deep model. Then the residual network is used to carry out deep learning on the fused multi-scale features to improve the residual network's ability to pay attention to important information, the self-attention mechanism is integrated into the residual network, and a new residual network structure is proposed. Finally, the maximum mean difference (MMD) is employed in output layer to measure the degree to which the probability distribution differs between the source and target domains to enhance the ability of model to transfer knowledge and complete the task of diagnosing the bearing of a machine. TL-IResnet is evaluated using the bearing dataset from Case Western Reserve University (CWRU) and the gearbox dataset from Southeast University. Experimental results demonstrate that TL-IResnet has a strong capacity to generalize information in addition to a high degree of accuracy under different conditions of operation, and has certain advantages over existing fault diagnosis methods.
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title_short	A cross-domain intelligent fault diagnosis method based on feature transfer with improved Inception ResNet for rolling bearings under varying working condition
url	https://doi.org/10.1299/jamdsm.2024jamdsm0012 https://doaj.org/article/c5735db7c3ae42b08fd028b36020dfb3 https://www.jstage.jst.go.jp/article/jamdsm/18/2/18_2024jamdsm0012/_pdf/-char/en https://doaj.org/toc/1881-3054
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A cross-domain intelligent fault diagnosis method based on feature transfer with improved Inception ResNet for rolling bearings under varying working condition

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