Bearing fault diagnosis method based on improved Siamese neural network with small sample

Abstract Fault diagnosis of rolling bearings is very important for monitoring the health of rotating machinery. However, in actual industrial production, owing to the constraints of conditions and costs, only a small number of bearing fault samples can be obtained, which leads to an unsatisfactory e...
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Gespeichert in:

Autor*in:	Zhao, Xiaoping [verfasserIn] Ma, Mengyao Shao, Fan

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Rolling bearing Siamese neural network Small sample Fault diagnosis

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: Journal of Cloud Computing - Berlin : SpringerOpen, 2012, 11(2022), 1 vom: 19. Nov.
Übergeordnetes Werk:	volume:11 ; year:2022 ; number:1 ; day:19 ; month:11

Links:	Volltext

DOI / URN:	10.1186/s13677-022-00350-1

Katalog-ID:	SPR048668710

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520			\|a Abstract Fault diagnosis of rolling bearings is very important for monitoring the health of rotating machinery. However, in actual industrial production, owing to the constraints of conditions and costs, only a small number of bearing fault samples can be obtained, which leads to an unsatisfactory effect of traditional fault diagnosis models based on data-driven methods. Therefore, this study proposes a small-sample bearing fault diagnosis method based on an improved Siamese neural network (ISNN). This method adds a classification branch to the standard Siamese network and replaces the common Euclidean distance measurement with a network measurement. The model includes three networks: a feature extraction network, a relationship measurement network, and a fault classification network. First, the fault samples were input into the same feature extraction network in pairs, and a long and short-term memory (LSTM) network and convolutional neural network (CNN) were used to map the bearing signal data to the low-dimensional feature space. Then, the extracted sample features were measured for similarity by the relationship measurement network; at the same time, the features were input into the classification network to complete the bearing fault recognition. When the number of training samples was particularly small (training set A, 10 samples), the accuracy of 1D CNN, Prototype net and Siamese net were 49.8%, 60.2% and 58.6% respectively, while the accuracy of the proposed ISNN method was 84.1%. For the 100-sample case of training set D, the accuracy of 1D CNN was improved to 93.4%, which was still higher than that of prototype and Siam network, while the accuracy of ISNN method reached 98.1%.The experimental results show that the method in this study achieved higher fault diagnosis accuracy and better generalization in the case of small samples.
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allfields	10.1186/s13677-022-00350-1 doi (DE-627)SPR048668710 (SPR)s13677-022-00350-1-e DE-627 ger DE-627 rakwb eng Zhao, Xiaoping verfasserin aut Bearing fault diagnosis method based on improved Siamese neural network with small sample 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Fault diagnosis of rolling bearings is very important for monitoring the health of rotating machinery. However, in actual industrial production, owing to the constraints of conditions and costs, only a small number of bearing fault samples can be obtained, which leads to an unsatisfactory effect of traditional fault diagnosis models based on data-driven methods. Therefore, this study proposes a small-sample bearing fault diagnosis method based on an improved Siamese neural network (ISNN). This method adds a classification branch to the standard Siamese network and replaces the common Euclidean distance measurement with a network measurement. The model includes three networks: a feature extraction network, a relationship measurement network, and a fault classification network. First, the fault samples were input into the same feature extraction network in pairs, and a long and short-term memory (LSTM) network and convolutional neural network (CNN) were used to map the bearing signal data to the low-dimensional feature space. Then, the extracted sample features were measured for similarity by the relationship measurement network; at the same time, the features were input into the classification network to complete the bearing fault recognition. When the number of training samples was particularly small (training set A, 10 samples), the accuracy of 1D CNN, Prototype net and Siamese net were 49.8%, 60.2% and 58.6% respectively, while the accuracy of the proposed ISNN method was 84.1%. For the 100-sample case of training set D, the accuracy of 1D CNN was improved to 93.4%, which was still higher than that of prototype and Siam network, while the accuracy of ISNN method reached 98.1%.The experimental results show that the method in this study achieved higher fault diagnosis accuracy and better generalization in the case of small samples. Rolling bearing (dpeaa)DE-He213 Siamese neural network (dpeaa)DE-He213 Small sample (dpeaa)DE-He213 Fault diagnosis (dpeaa)DE-He213 Ma, Mengyao aut Shao, Fan aut Enthalten in Journal of Cloud Computing Berlin : SpringerOpen, 2012 11(2022), 1 vom: 19. Nov. (DE-627)726491810 (DE-600)2682472-3 2192-113X nnns volume:11 year:2022 number:1 day:19 month:11 https://dx.doi.org/10.1186/s13677-022-00350-1 kostenfrei 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_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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 1 19 11
spelling	10.1186/s13677-022-00350-1 doi (DE-627)SPR048668710 (SPR)s13677-022-00350-1-e DE-627 ger DE-627 rakwb eng Zhao, Xiaoping verfasserin aut Bearing fault diagnosis method based on improved Siamese neural network with small sample 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Fault diagnosis of rolling bearings is very important for monitoring the health of rotating machinery. However, in actual industrial production, owing to the constraints of conditions and costs, only a small number of bearing fault samples can be obtained, which leads to an unsatisfactory effect of traditional fault diagnosis models based on data-driven methods. Therefore, this study proposes a small-sample bearing fault diagnosis method based on an improved Siamese neural network (ISNN). This method adds a classification branch to the standard Siamese network and replaces the common Euclidean distance measurement with a network measurement. The model includes three networks: a feature extraction network, a relationship measurement network, and a fault classification network. First, the fault samples were input into the same feature extraction network in pairs, and a long and short-term memory (LSTM) network and convolutional neural network (CNN) were used to map the bearing signal data to the low-dimensional feature space. Then, the extracted sample features were measured for similarity by the relationship measurement network; at the same time, the features were input into the classification network to complete the bearing fault recognition. When the number of training samples was particularly small (training set A, 10 samples), the accuracy of 1D CNN, Prototype net and Siamese net were 49.8%, 60.2% and 58.6% respectively, while the accuracy of the proposed ISNN method was 84.1%. For the 100-sample case of training set D, the accuracy of 1D CNN was improved to 93.4%, which was still higher than that of prototype and Siam network, while the accuracy of ISNN method reached 98.1%.The experimental results show that the method in this study achieved higher fault diagnosis accuracy and better generalization in the case of small samples. Rolling bearing (dpeaa)DE-He213 Siamese neural network (dpeaa)DE-He213 Small sample (dpeaa)DE-He213 Fault diagnosis (dpeaa)DE-He213 Ma, Mengyao aut Shao, Fan aut Enthalten in Journal of Cloud Computing Berlin : SpringerOpen, 2012 11(2022), 1 vom: 19. Nov. (DE-627)726491810 (DE-600)2682472-3 2192-113X nnns volume:11 year:2022 number:1 day:19 month:11 https://dx.doi.org/10.1186/s13677-022-00350-1 kostenfrei 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_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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 1 19 11
allfields_unstemmed	10.1186/s13677-022-00350-1 doi (DE-627)SPR048668710 (SPR)s13677-022-00350-1-e DE-627 ger DE-627 rakwb eng Zhao, Xiaoping verfasserin aut Bearing fault diagnosis method based on improved Siamese neural network with small sample 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Fault diagnosis of rolling bearings is very important for monitoring the health of rotating machinery. However, in actual industrial production, owing to the constraints of conditions and costs, only a small number of bearing fault samples can be obtained, which leads to an unsatisfactory effect of traditional fault diagnosis models based on data-driven methods. Therefore, this study proposes a small-sample bearing fault diagnosis method based on an improved Siamese neural network (ISNN). This method adds a classification branch to the standard Siamese network and replaces the common Euclidean distance measurement with a network measurement. The model includes three networks: a feature extraction network, a relationship measurement network, and a fault classification network. First, the fault samples were input into the same feature extraction network in pairs, and a long and short-term memory (LSTM) network and convolutional neural network (CNN) were used to map the bearing signal data to the low-dimensional feature space. Then, the extracted sample features were measured for similarity by the relationship measurement network; at the same time, the features were input into the classification network to complete the bearing fault recognition. When the number of training samples was particularly small (training set A, 10 samples), the accuracy of 1D CNN, Prototype net and Siamese net were 49.8%, 60.2% and 58.6% respectively, while the accuracy of the proposed ISNN method was 84.1%. For the 100-sample case of training set D, the accuracy of 1D CNN was improved to 93.4%, which was still higher than that of prototype and Siam network, while the accuracy of ISNN method reached 98.1%.The experimental results show that the method in this study achieved higher fault diagnosis accuracy and better generalization in the case of small samples. Rolling bearing (dpeaa)DE-He213 Siamese neural network (dpeaa)DE-He213 Small sample (dpeaa)DE-He213 Fault diagnosis (dpeaa)DE-He213 Ma, Mengyao aut Shao, Fan aut Enthalten in Journal of Cloud Computing Berlin : SpringerOpen, 2012 11(2022), 1 vom: 19. Nov. (DE-627)726491810 (DE-600)2682472-3 2192-113X nnns volume:11 year:2022 number:1 day:19 month:11 https://dx.doi.org/10.1186/s13677-022-00350-1 kostenfrei 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_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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 1 19 11
allfieldsGer	10.1186/s13677-022-00350-1 doi (DE-627)SPR048668710 (SPR)s13677-022-00350-1-e DE-627 ger DE-627 rakwb eng Zhao, Xiaoping verfasserin aut Bearing fault diagnosis method based on improved Siamese neural network with small sample 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Fault diagnosis of rolling bearings is very important for monitoring the health of rotating machinery. However, in actual industrial production, owing to the constraints of conditions and costs, only a small number of bearing fault samples can be obtained, which leads to an unsatisfactory effect of traditional fault diagnosis models based on data-driven methods. Therefore, this study proposes a small-sample bearing fault diagnosis method based on an improved Siamese neural network (ISNN). This method adds a classification branch to the standard Siamese network and replaces the common Euclidean distance measurement with a network measurement. The model includes three networks: a feature extraction network, a relationship measurement network, and a fault classification network. First, the fault samples were input into the same feature extraction network in pairs, and a long and short-term memory (LSTM) network and convolutional neural network (CNN) were used to map the bearing signal data to the low-dimensional feature space. Then, the extracted sample features were measured for similarity by the relationship measurement network; at the same time, the features were input into the classification network to complete the bearing fault recognition. When the number of training samples was particularly small (training set A, 10 samples), the accuracy of 1D CNN, Prototype net and Siamese net were 49.8%, 60.2% and 58.6% respectively, while the accuracy of the proposed ISNN method was 84.1%. For the 100-sample case of training set D, the accuracy of 1D CNN was improved to 93.4%, which was still higher than that of prototype and Siam network, while the accuracy of ISNN method reached 98.1%.The experimental results show that the method in this study achieved higher fault diagnosis accuracy and better generalization in the case of small samples. Rolling bearing (dpeaa)DE-He213 Siamese neural network (dpeaa)DE-He213 Small sample (dpeaa)DE-He213 Fault diagnosis (dpeaa)DE-He213 Ma, Mengyao aut Shao, Fan aut Enthalten in Journal of Cloud Computing Berlin : SpringerOpen, 2012 11(2022), 1 vom: 19. Nov. (DE-627)726491810 (DE-600)2682472-3 2192-113X nnns volume:11 year:2022 number:1 day:19 month:11 https://dx.doi.org/10.1186/s13677-022-00350-1 kostenfrei 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_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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 1 19 11
allfieldsSound	10.1186/s13677-022-00350-1 doi (DE-627)SPR048668710 (SPR)s13677-022-00350-1-e DE-627 ger DE-627 rakwb eng Zhao, Xiaoping verfasserin aut Bearing fault diagnosis method based on improved Siamese neural network with small sample 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Fault diagnosis of rolling bearings is very important for monitoring the health of rotating machinery. However, in actual industrial production, owing to the constraints of conditions and costs, only a small number of bearing fault samples can be obtained, which leads to an unsatisfactory effect of traditional fault diagnosis models based on data-driven methods. Therefore, this study proposes a small-sample bearing fault diagnosis method based on an improved Siamese neural network (ISNN). This method adds a classification branch to the standard Siamese network and replaces the common Euclidean distance measurement with a network measurement. The model includes three networks: a feature extraction network, a relationship measurement network, and a fault classification network. First, the fault samples were input into the same feature extraction network in pairs, and a long and short-term memory (LSTM) network and convolutional neural network (CNN) were used to map the bearing signal data to the low-dimensional feature space. Then, the extracted sample features were measured for similarity by the relationship measurement network; at the same time, the features were input into the classification network to complete the bearing fault recognition. When the number of training samples was particularly small (training set A, 10 samples), the accuracy of 1D CNN, Prototype net and Siamese net were 49.8%, 60.2% and 58.6% respectively, while the accuracy of the proposed ISNN method was 84.1%. For the 100-sample case of training set D, the accuracy of 1D CNN was improved to 93.4%, which was still higher than that of prototype and Siam network, while the accuracy of ISNN method reached 98.1%.The experimental results show that the method in this study achieved higher fault diagnosis accuracy and better generalization in the case of small samples. Rolling bearing (dpeaa)DE-He213 Siamese neural network (dpeaa)DE-He213 Small sample (dpeaa)DE-He213 Fault diagnosis (dpeaa)DE-He213 Ma, Mengyao aut Shao, Fan aut Enthalten in Journal of Cloud Computing Berlin : SpringerOpen, 2012 11(2022), 1 vom: 19. Nov. (DE-627)726491810 (DE-600)2682472-3 2192-113X nnns volume:11 year:2022 number:1 day:19 month:11 https://dx.doi.org/10.1186/s13677-022-00350-1 kostenfrei 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_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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 1 19 11
language	English
source	Enthalten in Journal of Cloud Computing 11(2022), 1 vom: 19. Nov. volume:11 year:2022 number:1 day:19 month:11
sourceStr	Enthalten in Journal of Cloud Computing 11(2022), 1 vom: 19. Nov. volume:11 year:2022 number:1 day:19 month:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Rolling bearing Siamese neural network Small sample Fault diagnosis
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container_title	Journal of Cloud Computing
authorswithroles_txt_mv	Zhao, Xiaoping @@aut@@ Ma, Mengyao @@aut@@ Shao, Fan @@aut@@
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author	Zhao, Xiaoping
spellingShingle	Zhao, Xiaoping misc Rolling bearing misc Siamese neural network misc Small sample misc Fault diagnosis Bearing fault diagnosis method based on improved Siamese neural network with small sample
authorStr	Zhao, Xiaoping
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topic_title	Bearing fault diagnosis method based on improved Siamese neural network with small sample Rolling bearing (dpeaa)DE-He213 Siamese neural network (dpeaa)DE-He213 Small sample (dpeaa)DE-He213 Fault diagnosis (dpeaa)DE-He213
topic	misc Rolling bearing misc Siamese neural network misc Small sample misc Fault diagnosis
topic_unstemmed	misc Rolling bearing misc Siamese neural network misc Small sample misc Fault diagnosis
topic_browse	misc Rolling bearing misc Siamese neural network misc Small sample misc Fault diagnosis
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title	Bearing fault diagnosis method based on improved Siamese neural network with small sample
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title_full	Bearing fault diagnosis method based on improved Siamese neural network with small sample
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author_browse	Zhao, Xiaoping Ma, Mengyao Shao, Fan
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doi_str_mv	10.1186/s13677-022-00350-1
title_sort	bearing fault diagnosis method based on improved siamese neural network with small sample
title_auth	Bearing fault diagnosis method based on improved Siamese neural network with small sample
abstract	Abstract Fault diagnosis of rolling bearings is very important for monitoring the health of rotating machinery. However, in actual industrial production, owing to the constraints of conditions and costs, only a small number of bearing fault samples can be obtained, which leads to an unsatisfactory effect of traditional fault diagnosis models based on data-driven methods. Therefore, this study proposes a small-sample bearing fault diagnosis method based on an improved Siamese neural network (ISNN). This method adds a classification branch to the standard Siamese network and replaces the common Euclidean distance measurement with a network measurement. The model includes three networks: a feature extraction network, a relationship measurement network, and a fault classification network. First, the fault samples were input into the same feature extraction network in pairs, and a long and short-term memory (LSTM) network and convolutional neural network (CNN) were used to map the bearing signal data to the low-dimensional feature space. Then, the extracted sample features were measured for similarity by the relationship measurement network; at the same time, the features were input into the classification network to complete the bearing fault recognition. When the number of training samples was particularly small (training set A, 10 samples), the accuracy of 1D CNN, Prototype net and Siamese net were 49.8%, 60.2% and 58.6% respectively, while the accuracy of the proposed ISNN method was 84.1%. For the 100-sample case of training set D, the accuracy of 1D CNN was improved to 93.4%, which was still higher than that of prototype and Siam network, while the accuracy of ISNN method reached 98.1%.The experimental results show that the method in this study achieved higher fault diagnosis accuracy and better generalization in the case of small samples. © The Author(s) 2022
abstractGer	Abstract Fault diagnosis of rolling bearings is very important for monitoring the health of rotating machinery. However, in actual industrial production, owing to the constraints of conditions and costs, only a small number of bearing fault samples can be obtained, which leads to an unsatisfactory effect of traditional fault diagnosis models based on data-driven methods. Therefore, this study proposes a small-sample bearing fault diagnosis method based on an improved Siamese neural network (ISNN). This method adds a classification branch to the standard Siamese network and replaces the common Euclidean distance measurement with a network measurement. The model includes three networks: a feature extraction network, a relationship measurement network, and a fault classification network. First, the fault samples were input into the same feature extraction network in pairs, and a long and short-term memory (LSTM) network and convolutional neural network (CNN) were used to map the bearing signal data to the low-dimensional feature space. Then, the extracted sample features were measured for similarity by the relationship measurement network; at the same time, the features were input into the classification network to complete the bearing fault recognition. When the number of training samples was particularly small (training set A, 10 samples), the accuracy of 1D CNN, Prototype net and Siamese net were 49.8%, 60.2% and 58.6% respectively, while the accuracy of the proposed ISNN method was 84.1%. For the 100-sample case of training set D, the accuracy of 1D CNN was improved to 93.4%, which was still higher than that of prototype and Siam network, while the accuracy of ISNN method reached 98.1%.The experimental results show that the method in this study achieved higher fault diagnosis accuracy and better generalization in the case of small samples. © The Author(s) 2022
abstract_unstemmed	Abstract Fault diagnosis of rolling bearings is very important for monitoring the health of rotating machinery. However, in actual industrial production, owing to the constraints of conditions and costs, only a small number of bearing fault samples can be obtained, which leads to an unsatisfactory effect of traditional fault diagnosis models based on data-driven methods. Therefore, this study proposes a small-sample bearing fault diagnosis method based on an improved Siamese neural network (ISNN). This method adds a classification branch to the standard Siamese network and replaces the common Euclidean distance measurement with a network measurement. The model includes three networks: a feature extraction network, a relationship measurement network, and a fault classification network. First, the fault samples were input into the same feature extraction network in pairs, and a long and short-term memory (LSTM) network and convolutional neural network (CNN) were used to map the bearing signal data to the low-dimensional feature space. Then, the extracted sample features were measured for similarity by the relationship measurement network; at the same time, the features were input into the classification network to complete the bearing fault recognition. When the number of training samples was particularly small (training set A, 10 samples), the accuracy of 1D CNN, Prototype net and Siamese net were 49.8%, 60.2% and 58.6% respectively, while the accuracy of the proposed ISNN method was 84.1%. For the 100-sample case of training set D, the accuracy of 1D CNN was improved to 93.4%, which was still higher than that of prototype and Siam network, while the accuracy of ISNN method reached 98.1%.The experimental results show that the method in this study achieved higher fault diagnosis accuracy and better generalization in the case of small samples. © The Author(s) 2022
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title_short	Bearing fault diagnosis method based on improved Siamese neural network with small sample
url	https://dx.doi.org/10.1186/s13677-022-00350-1
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author2	Ma, Mengyao Shao, Fan
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up_date	2024-07-03T20:42:53.658Z
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Bearing fault diagnosis method based on improved Siamese neural network with small sample

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