Cross-Conditions Fault Diagnosis of Rolling Bearings Based on Dual Domain Adversarial Network

To address the problem of traditional diagnosis methods being ineffective due to the feature distribution shift of rolling bearings under cross-conditions, a new method called the dual domain adversarial network (DDAN) has been proposed in this study. The DDAN is integrated with a multichannel parallel feature extractor, which can mine domain-invariant features, extracting as many useful features as possible from both the frequency-domain and the time-frequency domain perspectives. The L(1,2 )norm based Wasserstein discrepancy ( L1,2 WD) is then introduced as the domain difference value to improve the stability and computation speed of the diagnosis model. After that, a dual domain adversarial paradigm is constructed to jump out of local superiority and improve the generalization of the model by correcting the overconfidence of the model and expanding the confidence interval, respectively. Finally, the verification is performed on two bearing datasets in comparison with several unsupervised domain adaptation (UDA) methods, and the outcomes demonstrate the excellence of DDAN in resolving cross-conditions rolling bearing fault diagnosis issues.