Multi-source Unsupervised Domain Adaptation for Machinery Fault Diagnosis under Different Working Conditions

Abstract

Owing to distribution discrepancy between source training and target testing data, the performance of fault diagnosis by traditional supervised learning models will degenerate. Though domain adaptation methods for diagnosis have been actively investigated recently, most of them are devoted to learning from a single source. However, in reality, the supervised samples can be collected from different sources such as various working conditions. These sources are not only different from target but also from each other. The way of effectively fusing these sources to contribute the prediction of target remains a challenge. In this work, a new framework of multi-source domain adaptation is proposed for cross-domain fault diagnosis under different working conditions. Specially, this framework is realized by two alignment stages. At the first stage, multiple specific feature spaces are obtained, then the distributions of each pair of source and target domain are aligned since it is difficult to extract the common domain-invariant features for all domains. At the second stage, by considering the domain specific decision boundaries, the probabilistic outputs of classifiers are also aligned. Various experimental analysis on four different bearing working conditions is conducted to show the effectiveness of the proposed method. The performance of the proposed method is superior to state-the-art cross-domain fault diagnosis methods.