Contrastive learning-based multi-source domain adaptation: a cross-domain fault diagnosis method for suppressing negative transfer

Abstract Traditional cross-domain transfer learning faces significant challenges in rotating machinery fault diagnosis due to the general scarcity of fault samples. Furthermore, under complex operating conditions, differences in data distribution between the source domain and target domain can also cause negative migration. To overcome these limitations, this paper proposes a multi-source contrastive learning domain adaptation (MSCLDA) method. Firstly, the MSCLDA approach employs a Wasserstein generative adversarial network to synthesize minority-class fault samples. Subsequently, it integrates a supervised contrastive learning strategy to optimize feature consistency between real and synthetic samples, thereby effectively mitigating the training bias caused by category imbalance. Furthermore, we devise a prototype-based multi-pseudo label self-correction mechanism. By incorporating this mechanism with a multiple pseudo-label-guided local maximum mean discrepancy strategy, we achieve precise alignment of feature distributions between the source domains and target subdomains. Finally, an adaptive weighting mechanism is introduced to assign higher weights to source domains that are more relevant to the target domain, thereby reducing the adverse impact of less relevant domains and mitigating negative transfer. Cross-domain experiments on current signals from industrial robots and vibration signals from bearings show that MSCLDA achieves superior fault identification performance under various working conditions, with an average accuracy of up to 99.05%, and effectively suppresses negative transfer.