Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing-Reference-Cited by-同舟云学术

Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

Published:2018-02 Issue: Volume:100 Page:743-765
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Shao Haidong,Jiang Hongkai,Zhang Haizhou,Duan Wenjing,Liang Tianchen,Wu Shuaipeng

Funder

National Natural Science Foundation of China

Shanghai Engineering Research Center of Civil Aircraft Health Monitoring Foundation of China

Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University

Publisher

Elsevier BV

Subject

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

Reference53 articles.

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3. Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings;Miao;Mech. Syst. Sig. Process.,2017

4. Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive;Li;Mech. Syst. Sig. Process.,2017

5. Wavelet transform based on inner product in fault diagnosis of rotating machinery: a review;Chen;Mech. Syst. Sig. Process.,2016

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