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
Published in:Shock and vibration Vol. 2020; no. 2020; pp. 1 - 12
Main Authors: Li, Shijie, Jiang, Shouda, Gao, Tianyu, Yang, Jingli, Tang, Qing
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Wiley
Subjects:
Accuracy
Adaptive algorithms
Algorithms
Artificial intelligence
Classification
Convolution
Decomposition
Deep learning
Fault diagnosis
Feature extraction
Interference
Machine learning
Machinery
Neural networks
Noise
Noise reduction
Rotating machinery
Shrinkage
ISSN:1070-9622, 1875-9203
Online Access:Get full text
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Summary: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 proposed. First, a one-dimensional deep residual shrinkage network, which directly takes the raw vibration signals contaminated by noise as input, is developed to realize end-to-end fault diagnosis. Then, to further enhance the noise immunity of the diagnosis model, the first layer of the model is set to a wide convolution layer to extract short time features. Moreover, an adaptive batch normalization algorithm (AdaBN) is introduced into the diagnosis model to enhance the adaptability to noise. Experimental results illustrate that the fault diagnosis model for rotating machinery based on one-dimensional deep residual shrinkage network with a wide convolution layer (1D-WDRSN) can accurately identify the fault classes even under noise interference.
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ISSN:1070-9622
1875-9203
DOI:10.1155/2020/8880960