Variable-Bandwidth Self-Convergent Variational Mode Decomposition and its Application to Fault Diagnosis of Rolling Bearing-Reference-Cited by-同舟云学术

Variable-Bandwidth Self-Convergent Variational Mode Decomposition and its Application to Fault Diagnosis of Rolling Bearing

Published:2024 Issue: Volume:73 Page:1-15
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Lv Yong¹^ORCID,Li Zhaolun¹^ORCID,Yuan Rui¹^ORCID,Zhang Qixiang²^ORCID,Wu Hongan¹^ORCID

Affiliation:

1. Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, and the Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China

2. Computer Engineering Technical College, and the Artificial Intelligence College, Guangdong Polytechnic of Science and Technology, Zhuhai, China

Funder

National Natural Science Foundation of China

Hubei Provincial Natural Science Foundation Youth Program

Innovation Group Program

Innovation Development Joint Key Program

Wuhan Key Research and Development Plan Artificial Intelligence Innovation Special Program

14th Five Year Plan Hubei Provincial Advantaged Characteristic Disciplines (Groups) Project of Wuhan University of Science and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10449478.pdf?arnumber=10449478

Reference55 articles.

1. A review on data-driven fault severity assessment in rolling bearings

2. A hybrid deep-learning model for fault diagnosis of rolling bearings

3. A review of wind turbine bearing condition monitoring: State of the art and challenges

4. Looseness monitoring of multiple M1 bolt joints using multivariate intrinsic multiscale entropy analysis and Lorentz signal-enhanced piezoelectric active sensing

5. Multiwavelet transform and its applications in mechanical fault diagnosis – A review

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