A Neural-Symbolic Network for Interpretable Fault Diagnosis of Rolling Element Bearings Based on Temporal Logic
Author:
Affiliation:
1. Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
2. Guangdong Rural Credit Union, Guangdong, China
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Basic and Applied Basic Research Foundation of Guangdong Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/19/10367905/10458993.pdf?arnumber=10458993
Reference30 articles.
1. A Concentrated Time–Frequency Analysis Tool for Bearing Fault Diagnosis
2. An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition
3. Open-Circuit Fault Diagnosis in Power Inverters Through Currents Analysis in Time Domain
4. Novel Machinery Monitoring Strategy Based on Time–Frequency Domain Similarity Measurement With Limited Labeled Data
5. Multiscale Wavelet Prototypical Network for Cross-Component Few-Shot Intelligent Fault Diagnosis
Cited by 2 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. Bearing Fault Diagnosis via Robust PCA With Nonconvex Rank Approximation;IEEE Sensors Journal;2024-06-15
2. Enhancing Reliability Through Interpretability: A Comprehensive Survey of Interpretable Intelligent Fault Diagnosis in Rotating Machinery;IEEE Access;2024
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