Robust Separation-Enhanced NRC Method for Multiple Periodicity Detection: Applications in Bearing Compound Fault Diagnosis-Reference-Cited by-同舟云学术

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Robust Separation-Enhanced NRC Method for Multiple Periodicity Detection: Applications in Bearing Compound Fault Diagnosis

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Chen Saisai¹^ORCID,Fan Wei¹^ORCID,Xiong Yuyong²^ORCID,Peng Zhike³^ORCID

Affiliation:

1. School of Mechanical Engineering, Jiangsu University, Zhenjiang, China

2. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

3. School of Mechanical Engineering, Ningxia University, Yinchuan, China

Funder

Research Project of State Key Laboratory of Mechanical System and Vibration

China Postdoctoral Science Foundation

Young Talent Cultivation Program of Jiangsu University

8th Young Elite Scientists Sponsorship Program by CAST

Jiangsu Provincial Graduate Research and Innovation Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10483095.pdf?arnumber=10483095

Reference24 articles.

1. Feature Extraction Based on Hierarchical Improved Envelope Spectrum Entropy for Rolling Bearing Fault Diagnosis

2. Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics

3. Instantaneous Frequency Synchronized Generalized Stepwise Demodulation Transform for Bearing Fault Diagnosis

4. Machinery cross domain degradation prognostics considering compound domain shifts

5. An Intelligent Method for Early Motor Bearing Fault Diagnosis Based on Wasserstein Distance Generative Adversarial Networks Meta Learning

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