Adaptive Feature-Oriented Dictionary Learning and Sparse Classification Framework for Bearing Compound Fault Diagnosis-Reference-Cited by-同舟云学术

Adaptive Feature-Oriented Dictionary Learning and Sparse Classification Framework for Bearing Compound Fault Diagnosis

Published:2024 Issue: Volume:73 Page:1-10
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Fan Wei¹^ORCID,Yang Chenxi²^ORCID,Chen Chao²^ORCID,He Changbo³^ORCID,Yuan Ye⁴^ORCID,Li Ying⁵^ORCID

Affiliation:

1. School of Mechanical Engineering, Jiangsu Province and Education Ministry Co-Sponsored Synergistic Innovation Center of Modern Agricultural Equipment, Jiangsu University, Zhenjiang, China

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

3. School of Electrical Engineering and Automation, Anhui University, Hefei, China

4. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China

5. Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing, China

Funder

Natural Science Foundation of China

Research Project of the State Key Laboratory of Mechanical System and Vibration

Eighth Young Elite Scientists Sponsorship Program by CAST

Project

China Postdoctoral Science Foundation

Natural Science Foundation of Anhui Province

Jiangsu Province and Education Ministry Co-Sponsored Synergistic Innovation Center of Modern Agriculture Equipment

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10486961.pdf?arnumber=10486961

Reference34 articles.

1. Feature extraction of gear and bearing compound faults based on vibration signal sparse decomposition

2. Development of Compound Fault Diagnosis System for Gearbox Based on Convolutional Neural Network

3. Multi-source fidelity sparse representation via convex optimization for gearbox compound fault diagnosis

4. Sparse feature extraction based on periodical convolutional sparse representation for fault detection of rotating machinery

5. Bearing Fault Diagnosis Based on Clustering and Sparse Representation in Frequency Domain

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