Light neural network with fewer parameters based on CNN for fault diagnosis of rotating machinery-Reference-Cited by-同舟云学术

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Light neural network with fewer parameters based on CNN for fault diagnosis of rotating machinery

Published:2021-08 Issue: Volume:181 Page:109639
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Jin Tongtong,Yan Chuliang,Chen Chuanhai,Yang Zhaojun,Tian Hailong,Wang Siyuan

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference54 articles.

1. A Hybrid Deep-Learning Model for Fault Diagnosis of Rolling Bearings;Xu;Measurement,2021

2. An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis;Kaplan;Appl. Soft Comput.,2020

3. A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearings;Shao;Mech. Syst. Sig. Process.,2018

4. A bearing data analysis based on kurtogram and deep learning sequence models;Udmale;Measurement,2019

5. A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery;Zhang;Mech. Syst. Sig. Process.,2018

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