Time-Domain Sparsity-Based Bearing Fault Diagnosis Methods Using Pulse Signal-to-Noise Ratio-Reference-Cited by-同舟云学术

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Time-Domain Sparsity-Based Bearing Fault Diagnosis Methods Using Pulse Signal-to-Noise Ratio

Published:2024 Issue: Volume:73 Page:1-4
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhang Chi¹^ORCID,Wei Shaoming²^ORCID,Dong Ge¹^ORCID,Zeng Yajun²^ORCID,Zhu Guohun³^ORCID,Zhou Xujuan⁴^ORCID,Liu Feng³^ORCID

Affiliation:

1. School of Aerospace Engineering, Tsinghua University, Beijing, China

2. School of Electronic and Information Engineering, Beihang University, Beijing, China

3. School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, QLD, Australia

4. School of Business, University of Southern Queensland, Toowoomba, QLD, Australia

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10486958.pdf?arnumber=10486958

Reference22 articles.

1. Current-Aided Order Tracking of Vibration Signals for Bearing Fault Diagnosis of Direct-Drive Wind Turbines

2. Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators

3. Rolling element bearing diagnostics—A tutorial

4. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study

5. Combined Square Envelope Spectrum by Integrating Multiband Bearing Fault Information

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Quasi-Periodic Component Decomposition: New Decomposition Theory for Gear Fault Diagnosis;IEEE Transactions on Instrumentation and Measurement;2024

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