An interpretable data augmentation scheme for machine fault diagnosis based on a sparsity-constrained generative adversarial network

•A novel SC-GAN data augmentation scheme for stable raw vibration signal generation.•An interpretation method to open the black box of the proposed neural network model.•The model can learn frequency domain patterns from time domain vibration signals.•Data augmentation improves the performance of the machine fault diagnosis model.