LAUSR

The deep learning-based fault diagnosis approaches have achieved remarkable success in the field of rotating machinery condition monitoring. However, the basic intelligent fault diagnosis approaches require plenty of labeled samples to learn the strong feature representations in embedding feature space. Besides, it is time-consuming and laborious to collect sufficient labeled training samples in industrial occasions, which tremendously limits its performance in actual applications. To address the shortcoming, a relation-based semisupervised method is proposed in this article. In the proposed method, the sample relation discovering and pseudo label learning are integrated into an iterative framework, which is used to perform labeled sample augmentation in a progressive manner. Afterward, the supervised transfer training is introduced to reduce distribution discrepancy between the labeled and unlabeled samples, and eventually realize fault classification. Two gearbox fault diagnosis experiments are carried out to evaluate the effectiveness of the proposed method. The experimental results indicate that the proposed method obtains remarkable fault classification accuracy even only 5% of the labeled samples are available, which outperforms traditionally supervised and semisupervised fault diagnosis approaches.