A novel self-supervised representation learning framework based on time-frequency alignment and interaction for mechanical fault diagnosis
Author:
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hubei Province
Huazhong University of Science and Technology
Publisher
Elsevier BV
Reference47 articles.
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1. Spatial-temporal graph feature learning driven by time–frequency similarity assessment for robust fault diagnosis of rotating machinery;Advanced Engineering Informatics;2024-10
2. Evaluation of Hand-Crafted Feature Extraction for Fault Diagnosis in Rotating Machinery: A Survey;Sensors;2024-08-21
3. Semi-supervised few-shot fault diagnosis driven by multi-head dynamic graph attention network under speed fluctuations;Digital Signal Processing;2024-08
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