A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network

This paper constructs a novel network structure (SVD-1DCNN) based on singular value decomposition (SVD) and one-dimensional convolutional neural network (1DCNN), which takes the original signal as input to realize intelligent diagnosis of bearing faults. The output of the first convolution layer was...

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Main Authors:	Yangyang Wang, Shuzhan Huang, Juying Dai, Jian Tang
Format:	Article
Language:	English
Published:	Hindawi Limited 2020-01-01
Series:	Shock and Vibration
Online Access:	http://dx.doi.org/10.1155/2020/1850286

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record_format	Article
spelling	doaj-5fa96d794e0040bfa47782958fc32a152020-11-25T01:42:28ZengHindawi LimitedShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/18502861850286A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural NetworkYangyang Wang0Shuzhan Huang1Juying Dai2Jian Tang3Xichang Satellite Launch Center, Xichang 615000, ChinaSchool of Graduate, Army Engineering University of PLA, Nanjing 210000, ChinaSchool of Field Engineering, Army Engineering University of PLA, Nanjing 21000, ChinaSchool of Field Engineering, Army Engineering University of PLA, Nanjing 21000, ChinaThis paper constructs a novel network structure (SVD-1DCNN) based on singular value decomposition (SVD) and one-dimensional convolutional neural network (1DCNN), which takes the original signal as input to realize intelligent diagnosis of bearing faults. The output of the first convolution layer was also analyzed from the perspectives of time domain and time-frequency domain in the simulation experiment. Through qualitative analysis and quantitative analysis, it was found that the convolution kernel not only extracted the classification features of signals but also gradually highlighted the learned features in the network training process. Moreover, applying this network in fault diagnosis of bearing date provided by the Case Western Reserve University (CWRU) Bearing Data Center, it was found that the convolution kernel could also achieve the above operation. The novel network of this paper achieved a good classification effect on both the simulated signals and the measured signals.http://dx.doi.org/10.1155/2020/1850286
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Yangyang Wang Shuzhan Huang Juying Dai Jian Tang
spellingShingle	Yangyang Wang Shuzhan Huang Juying Dai Jian Tang A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network Shock and Vibration
author_facet	Yangyang Wang Shuzhan Huang Juying Dai Jian Tang
author_sort	Yangyang Wang
title	A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network
title_short	A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network
title_full	A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network
title_fullStr	A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network
title_full_unstemmed	A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network
title_sort	novel bearing fault diagnosis methodology based on svd and one-dimensional convolutional neural network
publisher	Hindawi Limited
series	Shock and Vibration
issn	1070-9622 1875-9203
publishDate	2020-01-01
description	This paper constructs a novel network structure (SVD-1DCNN) based on singular value decomposition (SVD) and one-dimensional convolutional neural network (1DCNN), which takes the original signal as input to realize intelligent diagnosis of bearing faults. The output of the first convolution layer was also analyzed from the perspectives of time domain and time-frequency domain in the simulation experiment. Through qualitative analysis and quantitative analysis, it was found that the convolution kernel not only extracted the classification features of signals but also gradually highlighted the learned features in the network training process. Moreover, applying this network in fault diagnosis of bearing date provided by the Case Western Reserve University (CWRU) Bearing Data Center, it was found that the convolution kernel could also achieve the above operation. The novel network of this paper achieved a good classification effect on both the simulated signals and the measured signals.
url	http://dx.doi.org/10.1155/2020/1850286
work_keys_str_mv	AT yangyangwang anovelbearingfaultdiagnosismethodologybasedonsvdandonedimensionalconvolutionalneuralnetwork AT shuzhanhuang anovelbearingfaultdiagnosismethodologybasedonsvdandonedimensionalconvolutionalneuralnetwork AT juyingdai anovelbearingfaultdiagnosismethodologybasedonsvdandonedimensionalconvolutionalneuralnetwork AT jiantang anovelbearingfaultdiagnosismethodologybasedonsvdandonedimensionalconvolutionalneuralnetwork AT yangyangwang novelbearingfaultdiagnosismethodologybasedonsvdandonedimensionalconvolutionalneuralnetwork AT shuzhanhuang novelbearingfaultdiagnosismethodologybasedonsvdandonedimensionalconvolutionalneuralnetwork AT juyingdai novelbearingfaultdiagnosismethodologybasedonsvdandonedimensionalconvolutionalneuralnetwork AT jiantang novelbearingfaultdiagnosismethodologybasedonsvdandonedimensionalconvolutionalneuralnetwork
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A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network

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