Markov Modeling of Signal Condition Transitions for Bearing Diagnostics Under External Interference Conditions-Reference-Cited by-同舟云学术

Markov Modeling of Signal Condition Transitions for Bearing Diagnostics Under External Interference Conditions

Published:2024 Issue: Volume:73 Page:1-8
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Chen Peng¹^ORCID,Wu Yuhao²^ORCID,Xu Chaojun²^ORCID,Jin Yaqiang³^ORCID,Zhou Chengning⁴^ORCID

Affiliation:

1. College of Engineering and the Key Laboratory of Intelligent Manufacturing Technology, Ministry of Education of China, Shantou University, Shantou, Guangdong, China

2. College of Engineering, Shantou University, Shantou, Guangdong, China

3. Center for Structural Acoustics and Machine Fault Diagnosis, Qingdao University of Technology, Qingdao, China

4. Nuclear Power Institute of China, Chengdu, China

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Guangdong Provincial Science and Technology Special Fund Project

Shantou University (STU) Scientific Research Initiation

Natural Science Foundation of Sichuan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10486940.pdf?arnumber=10486940

Reference26 articles.

1. Cyclomap: A new phase-cycle analysis to study the kinematics of gears and bearings

2. An ameliorated synchroextracting transform based on upgraded local instantaneous frequency approximation

3. A knowledge dynamic matching unit-guided multi-source domain adaptation network with attention mechanism for rolling bearing fault diagnosis

4. A threshold self-setting condition monitoring scheme for wind turbine generator bearings based on deep convolutional generative adversarial networks

5. An automatic speed adaption neural network model for planetary gearbox fault diagnosis