Pair-Wise Orthogonal Classifier Based Domain Adaptation Network for Fault Diagnosis in Rotating Machinery

Although machine learning methods have demonstrated their effectiveness in fault diagnosis in rotating machinery, there is a major assumption that the training data (source domain) and testing data (target domain) should share the same distribution. However, this assumption is difficult to hold in real scenarios considering the variable working conditions, and it recasts the fault diagnosis problem in a cross-domain manner. Recently, the adversarial domain adaptation methods have become a hot research topic, since they aim to address cross-domain issues and can be well embedded into convolutional neural networks. Most previous studies aimed to achieve the optimal alignment of data in a global view. Unfortunately, they may affect the data which are originally well aligned in the local view between the source domain and the target domain, thus leading to diminished diagnosis performance. In this paper, a pair-wise orthogonal classifier based domain adaptation network is proposed to address this issue. A feature extractor together with a pair-wise orthogonal classifier is designed to learn domain-invariant features from the source domain and the target domain. Then, based on the outputs of the pair-wise classifier, a dynamic weighted domain discriminator is designed to form an adversarial framework with a feature extractor. It considers the sample-level alignment in the domain adaptation process and enables the global alignment without sacrificing the original well-aligned data. Cross-domain experiments via two datasets are carried out to validate the performance of the proposed network. Performance comparisons with state-of-the-art methods are also made. The results have demonstrated the effectiveness and novelty of the proposed network.