A novel source-free domain adaptation with high-confidence sample selection and feature disentanglement for machinery fault diagnosis
Motivated by privacy concerns and the high cost of measured data transmission, source-free domain adaptation (SFDA) has attracted increasing attention for intelligent fault diagnosis. Instead of accessing raw measured source-domain data, SFDA transfers knowledge from pre-trained source models to target domains. However...