Multi-stage distribution correction: A promising data augmentation method for few-shot fault diagnosis

Abstract

Benefiting from the excellent capability of data processing, deep learning-based methods have been well applied in fault diagnosis. However, these methods may perform poorly due to lack of labeled data for training in real-world applications In few-shot learning settings, these methods may be trapped in overfitting since the data distribution estimated from a small number of labeled samples may be overly biased, which cannot cover the ground-truth data distribution. Given this problem, we propose a data augmentation method named Multi-Stage Distribution Correction (MSDC) for few-shot fault diagnosis. The proposed method can be divided into four training stages. In the first stage, unlabeled query features are clustered into several groups via an unsupervised Fuzzy C-means algorithm. Secondly, according to cosine similarity, the few labeled support features are assigned to the cluster with the highest similarity. Then, the Gaussian statistics of each cluster are extracted to correct the distributions of support features. Specifically, Our proposed method assumes that each dimension of the feature representation from the same class follows Gaussian distribution. New labeled features are generated by sampling from the corrected distributions. These generated features, along with the labeled support features, are used to train the task-specific classifier in the last stage. By joint training of the four stages, the scale of the labeled training dataset is effectively expanded, and the classification performance of the proposed method can be enhanced. The experimental results reveal that the proposed method outperforms the selected comparative methods in two case studies.