Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

In actual engineering applications, inevitable noise seriously affects the accuracy of fault diagnosis for rotating machinery. To effectively identify the fault classes of rotating machinery under noise interference, an efficient fault diagnosis method without additional denoising procedures is prop...

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Bibliographic Details
Main Authors:	Jingli Yang, Tianyu Gao, Shouda Jiang, Shijie Li, Qing Tang
Format:	Article
Language:	English
Published:	Hindawi Limited 2020-01-01
Series:	Shock and Vibration
Online Access:	http://dx.doi.org/10.1155/2020/8880960

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