Cross Machine Transfer Fault Diagnosis by Ensemble Weighting Subdomain Adaptation Network

Many domain adaptation models have been explored for fault transfer diagnosis. However, most of them only consider the global domain adaptation of two domains while neglecting the fine-grained class-wise distribution alignment between the source and target domains. Thus, these models cannot satisfy the diagnostic requirement in some cases. In this article, a new ensemble weighting subdomain adaptation network (EWSAN) diagnostic model is established to improve the degree of domain confusion. In EWSAN, an enhanced joint distribution alignment (EJDA) mechanism is proposed. A multiscale top classifier with multiple diverse branches is designed based on ensemble learning to better achieve EJDA. Ensemble voting with the multiscale top classifier can obtain more reliable pseudolabels in the EJDA mechanism. An ensemble weighting maximum mean discrepancy with the class weight is constructed to enhance the fine-grained domain confusion. Moreover, the closed and partial transfer diagnostic tasks are made available. Furthermore, the information entropy is introduced to increase the confidence coefficient of the pseudo label. The proposed EWSAN diagnostic model is evaluated via multiple closed and partial fault transfer diagnosis experiments cross machines. The experimental results validate its effectiveness and superiority.