Physics-Informed Time-Frequency Fusion Network With Attention for Noise-Robust Bearing Fault Diagnosis
Author:
Affiliation:
1. Department of Mechanical and Control Engineering, Handong Global University, Pohang, South Korea
2. School of Mechanical and Control Engineering, Handong Global University, Pohang, South Korea
Funder
Korea Institute of Energy Technology Evaluation and Planning
Ministry of Trade, Industry & Energy (MOTIE), Republic of Korea
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10401875.pdf?arnumber=10401875
Reference50 articles.
1. Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images
2. Low speed bearing fault diagnosis using acoustic emission sensors
3. Artificial intelligence for fault diagnosis of rotating machinery: A review
4. Rolling Bearing Fault Classification Based on Envelope Spectrum and Support Vector Machine
5. Fault feature extraction and classification based on WPT and SVD: Application to element bearings with artificially created faults under variable conditions
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. Wavelet-Enhanced Time-Frequency Analysis Method for Bearing Fault Detection of Rotating Machinery;IEEE Access;2024
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