Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network.

The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCN...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jing Yan, Tingliang Liu, Xinyu Ye, Qianzhen Jing, Yuannan Dai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a03543f87a544c40b1e2f7391fcfb6ba
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a03543f87a544c40b1e2f7391fcfb6ba
record_format	dspace
spelling	oai:doaj.org-article:a03543f87a544c40b1e2f7391fcfb6ba2021-12-02T20:17:32ZRotating machinery fault diagnosis based on a novel lightweight convolutional neural network.1932-620310.1371/journal.pone.0256287https://doaj.org/article/a03543f87a544c40b1e2f7391fcfb6ba2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256287https://doaj.org/toc/1932-6203The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of rotating machinery, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, light-weight convolution blocks are constructed through basic elements such as spatial separable convolutions with the aim to effectively reduce model parameters. Secondly, the LCNN model for the intelligent fault diagnosis is constructed via lightweight convolution blocks instead of the tradi-tional convolution operation. Finally, to address the "black box" problem, the entire network is visualized through Tensorboard and t-distribution stochastic neighbor embedding. The results demonstrate that when the number of lightweight convolutional blocks reaches 6, the diagnosis accuracy of the LCNN model exceeds 99.9%. And the proposed model has become the most robust with parameters significantly decreasing. Furthermore, the proposed LCNN model has realized accurate, automatic, and robust fault diagnosis of rotating machinery, which makes it more suitable for deployment under the IIoT context.Jing YanTingliang LiuXinyu YeQianzhen JingYuannan DaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0256287 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jing Yan Tingliang Liu Xinyu Ye Qianzhen Jing Yuannan Dai Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network.
description	The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of rotating machinery, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, light-weight convolution blocks are constructed through basic elements such as spatial separable convolutions with the aim to effectively reduce model parameters. Secondly, the LCNN model for the intelligent fault diagnosis is constructed via lightweight convolution blocks instead of the tradi-tional convolution operation. Finally, to address the "black box" problem, the entire network is visualized through Tensorboard and t-distribution stochastic neighbor embedding. The results demonstrate that when the number of lightweight convolutional blocks reaches 6, the diagnosis accuracy of the LCNN model exceeds 99.9%. And the proposed model has become the most robust with parameters significantly decreasing. Furthermore, the proposed LCNN model has realized accurate, automatic, and robust fault diagnosis of rotating machinery, which makes it more suitable for deployment under the IIoT context.
format	article
author	Jing Yan Tingliang Liu Xinyu Ye Qianzhen Jing Yuannan Dai
author_facet	Jing Yan Tingliang Liu Xinyu Ye Qianzhen Jing Yuannan Dai
author_sort	Jing Yan
title	Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network.
title_short	Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network.
title_full	Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network.
title_fullStr	Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network.
title_full_unstemmed	Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network.
title_sort	rotating machinery fault diagnosis based on a novel lightweight convolutional neural network.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a03543f87a544c40b1e2f7391fcfb6ba
work_keys_str_mv	AT jingyan rotatingmachineryfaultdiagnosisbasedonanovellightweightconvolutionalneuralnetwork AT tingliangliu rotatingmachineryfaultdiagnosisbasedonanovellightweightconvolutionalneuralnetwork AT xinyuye rotatingmachineryfaultdiagnosisbasedonanovellightweightconvolutionalneuralnetwork AT qianzhenjing rotatingmachineryfaultdiagnosisbasedonanovellightweightconvolutionalneuralnetwork AT yuannandai rotatingmachineryfaultdiagnosisbasedonanovellightweightconvolutionalneuralnetwork
_version_	1718374362621411328

Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network.

Ejemplares similares