Feature-level consistency regularized Semi-supervised scheme with data augmentation for intelligent fault diagnosis under small samples

Intelligent fault diagnosis based on machine learning has yielded a wealth of research results. However, fault diagnosis under small samples is still a challenging problem due to the difficulty of collecting fault data of machines in engineering scenarios. To address this problem, this paper proposes a feature-level consistency regularized semi-supervised scheme with data augmentation for fault diagnosis of machines. In the proposed method, generative adversarial networks generate unlabeled data to augment the limited training set. The fault identification network in the proposed method is trained by the augmented data and a small amount of real data collected from machines in a semi-supervised learning manner. We construct a distance evaluation metric based on the Earth Mover's Distance (EMD) and further design a novel feature consistency regularization module, which helps the fault identification network learn robust fault features by minimizing the EMD between generated data features and real data features. To verify the effectiveness of the proposed model, two mechanical fault simulation experiments were carried out. The experimental results show that the proposed method achieves high fault diagnosis accuracy using only a small amount of training data samples.