Cross-Domain Open-Set Machinery Fault Diagnosis Based on Adversarial Network With Multiple Auxiliary Classifiers

Cross-domain fault diagnosis methods based on transfer learning attempt to leverage knowledge from a domain with sufficient labeled samples to a different but related domain with few or even nonlabeled samples. These methods have been widely investigated in the past years. Notwithstanding the efficacy, most existing approaches as- sume that the label spaces of training and testing data are the same. However, this assumption is not practical in actual applications because new fault category usually hap- pens in the testing stage. A cross-domain open-set transfer diagnosis method is presented in this article to manage the aforementioned problem. Domain adversarial model is employed to discriminate known from unknown target in- stances. Moreover, multiple auxiliary classifiers introduce a weighting module to evaluate the distinguishing domain knowledge to provide target instances with representative weights. The new adversarial domain adaptation network with diverse supplementary classifiers can effectively iden- tify the unknown and known fault categories in the target domain and bridge the domain shift between the common fault category of the source and target domain. Experi- ments on two bearing datasets show the effectiveness and advantage of the proposed method.