Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection-Reference-Cited by-同舟云学术

Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection

Published:2020-08 Issue: Volume:142 Page:106740
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Li Xueyi^ORCID,Li Jialin^ORCID,Zhao Chengying,Qu Yongzhi,He David^ORCID

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference41 articles.

1. Detection of pitting in gears using a deep sparse autoencoder;Qu;Appl. Sci.,2017

2. Fault diagnosis in spur gears based on genetic algorithm and random forest;Cerrada;Mech. Syst. Signal. Process.,2016

3. Time-varying mesh stiffness calculation of cracked spur gears;Ma;Eng. Fail. Anal.,2014

4. Review on dynamics of cracked gear systems;Ma;Eng. Fail. Anal.,2015

5. Multisynchrosqueezing transform;Yu;IEEE Trans. Ind. Electron.,2018

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