A multi-source domain adaption intelligent fault diagnosis method based on asymmetric adversarial training

Abstract To enhance the cross-domain diagnostic ability of the model, domain adaptation method is adopted. When using traditional domain adaption methods to extract domain invariant characteristics of axial flow fan faults, the characteristics of the source and target domains will be close to each other, thereby the distribution of trained source domain characteristics will be changed. When the fault characteristics of the source domain gather at the classification boundary, the trained model will incorrectly classify some target samples. In addition, single source domain adaptation can lead to poor model generalization ability. To resolve the above issues, a multi-source domain adaption intelligent fault diagnosis method based on asymmetric adversarial training is proposed. In this method, the asymmetric adversarial training method is used to realize unidirectional movement of fault characteristics from the target domain to the source domain; triplet-center loss is used to expand the inter-class distance and shorten the intra-class distance of fault characteristics in the source domain; domain invariant characteristics are extracted from different source and target domains, and they are inputted to their respective fault classifiers, then aligning the outputs of each classifier using cosine similarity. To improve the cross-domain diagnostic ability of the model, a strategy of aligning weights is adopted. The industrial actual data verification results indicate that this method is effective in solving relevant practical industrial problems.