ICPHM’23 Benchmark Vibration Dataset Applicable in Machine Learning for Systems’ Health Monitoring
Author:
Affiliation:
1. Concordia University,Canada
2. Luleå University of Technology,Sweden
3. Mälardalen University,Sweden
4. Mälardalen Industrial Technology Center,Sweden
5. Western New England University,USA
6. Eastern Washington University,USA
7. CableLabs,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10625768/10626308/10626846.pdf?arnumber=10626846
Reference25 articles.
1. Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising
2. A new hybrid deep signal processing approach for bearing fault diagnosis using vibration signals
3. Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis
4. Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning
5. Analytical vibration signal model and signature analysis in resonance region for planetary gearbox 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. Inter-class Metric Learning based Depth-wise Convolutional Neural Network for Gear Fault Diagnosis;2024 IEEE International Conference on Prognostics and Health Management (ICPHM);2024-06-17
2. Classification of Rotor Imbalance in Trains Using Airborne Sound With Real-World Data;2024 IEEE International Conference on Prognostics and Health Management (ICPHM);2024-06-17
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