Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis-Reference-Cited by-同舟云学术

Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis

Published:2023-02 Issue: Volume:230 Page:108969
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Guo Junchao^ORCID,He Qingbo^ORCID,Zhen Dong^ORCID,Gu Fengshou^ORCID,Ball Andrew D.

Funder

National Major Science and Technology Projects of China

National Program for Support of Top-notch Young Professionals

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference42 articles.

1. Remaining useful life prediction based on a multi-sensor data fusion model;Li;Reliab Eng Syst Saf,2021

2. Intelligent fault detection for rotating machinery using cyclic morphological modulation spectrum and hierarchical Teager permutation entropy;Guo;IEEE Trans Ind Inform,2022

3. Multi-scale deep intra-class transfer learning for bearing fault diagnosis;Wang;Reliab Eng Syst Saf,2020

4. Fast spectral correlation detector for periodic impulse extraction of rotating machinery;Guo;IEEE Trans Instrum Meas,2022

5. A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions;Miao;Reliab Eng Syst Saf,2022

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