A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in e...

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Bibliographic Details
Main Authors: Shoucong Xiong, Hongdi Zhou, Shuai He, Leilei Zhang, Qi Xia, Jianping Xuan, Tielin Shi
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Sensors
Subjects:
fault diagnosis
bearing
deep learning
convolutional neural network
wavelet packet transform
sensor signal
Online Access:https://www.mdpi.com/1424-8220/20/17/4965

Internet

https://www.mdpi.com/1424-8220/20/17/4965

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