Kai-Sheng Deng

Bearing Single-Source Domain Generalization Fault Diagnosis Method Based on Adaptive Frequency-Domain Augmentation and Unsupervised Contrastive Learning

An unsupervised contrastive learning framework named FDACL is proposed, providing an unsupervised diagnostic approach for railway bearings under unknown working conditions and outperforms state-of-the-art baselines on SDG transfer tasks.