A Novel Multiview Predictive Local Adversarial Network for Partial Transfer Learning in Cross-Domain Fault Diagnostics

Abstract

In recent years, transfer learning technology has been widely developed in the field of fault diagnosis and detection. At present, most transfer learning methods are based on the assumption that the source domain and the target domain share the same label space. However, in the industrial production process, it is more common that the label space of the target domain is a subset of the label space of the source domain, which is usually called partial transfer learning. In this article, a new multiview predictive local adversarial network (MPLAN) is proposed to solve this problem. The proposed method aligns the source domain and the target domain by local adversarial training where the features of normal states in the source domain and target domain are used to implement adversarial training to learn domain-invariant feature representations and avoid negative transfer caused by outlier classes. Moreover, two classifiers, which have different viewpoints, are used to predict the same sample in order to map the raw data to a better feature space. Weights of two classifiers are made a constraint in order to guarantee that two classifiers own different viewpoints. In order to verify the effectiveness of the proposed method, two datasets, which contain one 1-D sample dataset and one multidimensional difficult dataset, are used to design experiments. Based on the experimental results on two datasets, the proposed MPLAN method can improve the baseline by about 10%.