Multiscale Transfer Voting Mechanism: A New Strategy for Domain Adaption
Yi Qin, Xin Wang, Quan Qian, Huayan Pu, Jun Luo
Abstract
Domain adaption models are widely applied to fault transfer diagnosis. However, the traditional domain adaption models can output only one high-dimensional transfer feature (TF); thus, it is difficult to capture domain-invariant information. Besides, using only one fully connected top classifier probably causes overfitting. Considering these two problems, in this article, we propose a multiscale transfer voting mechanism (MSTVM) to improve the classical domain adaption models and it can be universally applicable to any one of most domain adaption models. MSTVM consists of two substrategies: multiscale transfer mechanism (MSTM) and multiple transfer voting mechanisms (MTVM). The MSTM block includes several branches with multiscale convolutional and pooling operations, and it can output several multiscale TFs to strengthen the domain confusion. The MTVM block consists of multiple top classifiers and a plurality voting operation; thus, MTVM can effectively avoid overfitting and improve generalization ability. MSTVM has the advantages of MSTM and MTVM. Via two transfer diagnosis experiments, the advantage of MSTVM for improving various domain adaption models is verified.