Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD-Reference-Cited by-同舟云学术

Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD

Published:2019-06 Issue: Volume:89 Page:256-271
ISSN:0019-0578
Container-title:ISA Transactions
language:en
Short-container-title:ISA Transactions

Author:

Zheng Kai,Luo Jiufei,Zhang Yi,Li Tianliang,Wen Jiafu,Xiao Hong

Funder

Chongqing Research Program of Basic Research and Frontier Technology, China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Control and Systems Engineering,Electrical and Electronic Engineering,Computer Science Applications,Instrumentation

Reference33 articles.

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2. SVD and Hankel matrix based de-noising approach for rolling bearing fault detection and its assessment using artificial faults;Reza;Mech. Syst. Signal Process.,2016

3. A novel procedure for diagnosing multiple faults in rotating machinery;Wang;ISA Trans,2015

4. Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis;Yi;Mech. Syst. Signal Process.,2015

5. The use of SESK as a trend parameter for localized bearing fault diagnosis in induction machines;Saidi;ISA Trans,2016

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