Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory

The traditional bearing fault diagnosis methods have complex operation processes and poor generalization ability, while the diagnosis accuracy of the existing intelligent diagnosis methods needs to be further improved. Therefore, a novel fault diagnosis approach named CNN-BLSTM for bearing is presen...

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Autores principales:	Dazhang You, Linbo Chen, Fei Liu, YePeng Zhang, Wei Shang, Yameng Hu, Wei Liu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/64e8317721a94aec9012bd81e05d7ce7
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id	oai:doaj.org-article:64e8317721a94aec9012bd81e05d7ce7
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spelling	oai:doaj.org-article:64e8317721a94aec9012bd81e05d7ce72021-11-22T01:10:51ZIntelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory1875-920310.1155/2021/7346352https://doaj.org/article/64e8317721a94aec9012bd81e05d7ce72021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7346352https://doaj.org/toc/1875-9203The traditional bearing fault diagnosis methods have complex operation processes and poor generalization ability, while the diagnosis accuracy of the existing intelligent diagnosis methods needs to be further improved. Therefore, a novel fault diagnosis approach named CNN-BLSTM for bearing is presented based on convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) in this paper. This method directly takes the collected one-dimensional raw vibration signal as input and adaptively extracts the feature information through CNN. Then, the BLSTM is used to fuse the extracted features to acquire the failure information sufficiently and prevent the model from overfitting. Finally, two different experimental datasets are used to verify the effectiveness of the method. The experimental results show that the proposed CNN-BLSTM model can accurately diagnose the fault category of bearings. It has the advantages of rapidity, stability, antinoise, and strong generalization.Dazhang YouLinbo ChenFei LiuYePeng ZhangWei ShangYameng HuWei LiuHindawi LimitedarticlePhysicsQC1-999ENShock and Vibration, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Dazhang You Linbo Chen Fei Liu YePeng Zhang Wei Shang Yameng Hu Wei Liu Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory
description	The traditional bearing fault diagnosis methods have complex operation processes and poor generalization ability, while the diagnosis accuracy of the existing intelligent diagnosis methods needs to be further improved. Therefore, a novel fault diagnosis approach named CNN-BLSTM for bearing is presented based on convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) in this paper. This method directly takes the collected one-dimensional raw vibration signal as input and adaptively extracts the feature information through CNN. Then, the BLSTM is used to fuse the extracted features to acquire the failure information sufficiently and prevent the model from overfitting. Finally, two different experimental datasets are used to verify the effectiveness of the method. The experimental results show that the proposed CNN-BLSTM model can accurately diagnose the fault category of bearings. It has the advantages of rapidity, stability, antinoise, and strong generalization.
format	article
author	Dazhang You Linbo Chen Fei Liu YePeng Zhang Wei Shang Yameng Hu Wei Liu
author_facet	Dazhang You Linbo Chen Fei Liu YePeng Zhang Wei Shang Yameng Hu Wei Liu
author_sort	Dazhang You
title	Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory
title_short	Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory
title_full	Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory
title_fullStr	Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory
title_full_unstemmed	Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory
title_sort	intelligent fault diagnosis of bearing based on convolutional neural network and bidirectional long short-term memory
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/64e8317721a94aec9012bd81e05d7ce7
work_keys_str_mv	AT dazhangyou intelligentfaultdiagnosisofbearingbasedonconvolutionalneuralnetworkandbidirectionallongshorttermmemory AT linbochen intelligentfaultdiagnosisofbearingbasedonconvolutionalneuralnetworkandbidirectionallongshorttermmemory AT feiliu intelligentfaultdiagnosisofbearingbasedonconvolutionalneuralnetworkandbidirectionallongshorttermmemory AT yepengzhang intelligentfaultdiagnosisofbearingbasedonconvolutionalneuralnetworkandbidirectionallongshorttermmemory AT weishang intelligentfaultdiagnosisofbearingbasedonconvolutionalneuralnetworkandbidirectionallongshorttermmemory AT yamenghu intelligentfaultdiagnosisofbearingbasedonconvolutionalneuralnetworkandbidirectionallongshorttermmemory AT weiliu intelligentfaultdiagnosisofbearingbasedonconvolutionalneuralnetworkandbidirectionallongshorttermmemory
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Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory

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