Data augmentation via variational mode reconstruction and its application in few-shot fault diagnosis of rolling bearings

Currently, the popular fault diagnosis methods based on deep learning encounter a common limitation in that their accuracy heavily relies on an adequate number of training samples. However, collecting fault samples in real-world scenarios is often challenging. To overcome this challenge, this paper develops a novel data augmentation method named variational mode reconstruction (VMR) to generate augmented samples with similar features to the original samples. The first key point is the random weighting of a certain randomly chosen intrinsic mode function (IMF). Another key point is that the mean values and standard deviations of the augmented samples remain consistent with the original samples. Next, the augmented balanced dataset is uti-lized to train a deep residual shrinkage network (DRSN), which is then employed for the classification of test samples. Finally, the effectiveness and superiority of the developed VMR in few-shot fault diagnosis are verified through a series of experiments.