To address the limitations of conventional convolutional neural network (CNN) in feature extraction of vibration signals and improve fault discriminative capability in adversarial domain adaptation in cross-domain fault diagnosis of rotating machinery, this study proposes a novel fault diagnosis approach based on a Multi-scale Conditional Domain Adversarial Network with Spectral Penalization (MCDANSP). The model directly uses raw vibration signals as input. Its feature extraction module combines multi-scale convolutions and a channel attention mechanism to enhance shared feature extraction between source and target domains. For knowledge transfer, the method uses a conditional domain adversarial network (CDAN) and adds batch spectral penalization (BSP) to preserve feature discriminability during domain alignment. Evaluation on the PU, PHM2009, and SEU datasets yielded accuracies of 82.96%, 84.63%, and 64.03%, respectively. These results surpassed other methods in this research, confirming the method’s superior performance.
Fang et al. (Sun,) studied this question.