CFFsBD: A Candidate Fault Frequencies-Based Blind Deconvolution for Rolling Element Bearings Fault Feature Enhancement-Reference-Cited by-同舟云学术

CFFsBD: A Candidate Fault Frequencies-Based Blind Deconvolution for Rolling Element Bearings Fault Feature Enhancement

Published:2023 Issue: Volume:72 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Cheng Yao¹^ORCID,Zhou Ning¹^ORCID,Wang Zhiwei²^ORCID,Chen Bingyan¹^ORCID,Zhang Weihua¹^ORCID

Affiliation:

1. Traction Power State Key Laboratory, Southwest Jiaotong University, Chengdu, China

2. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Sichuan Province

Open Project of Hebei Provincial Key Laboratory of Traffic Engineering Structural Mechanical Behavior Evolution and Control, Shijiazhuang Tiedao University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10035908.pdf?arnumber=10035908

Reference24 articles.

1. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection

2. A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

3. A geometrical investigation on the generalized l p /l q norm for blind deconvolution

4. Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings

5. Blind deconvolution based on cyclostationarity maximization and its application to fault identification

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