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

Abstract

Vibration signal-based methods have been widely utilized in machine fault diagnosis. Usually, a lack of sufficient training data can prevent these methods from achieving satisfactory performance. The generative adversarial network (GAN) is a feasible solution to this problem. However, existing GAN-based methods struggle to stably generate raw vibration signals. To achieve vibration signal generation, a novel sparsity-constrained GAN (SC-GAN) method containing a two-stage training process is developed, which can perform data augmentation for machine fault diagnosis with a simple structure. Autoencoder (AE)-based pretraining and sparsity regularization constraints are implemented in the proposed method. Furthermore, to understand the internal mechanisms of vibration signal generation, we propose a method for analyzing the network’s weight matrix to interpret the generation mechanism. In a case study on rolling element bearings, the SC-GAN is verified to be able to generate raw vibration signals under 10 different health conditions with a more stable training process than other models. In a fault diagnosis task, the data augmentation by SC-GAN significantly improves the diagnostic accuracy by 7.44%. An analysis of the well-trained SC-GAN shows that the model captures key frequency components, which provides a credible interpretation for the generation mechanism. Another case study on the gearbox illustrates the good generalization ability of SC-GAN to other machines and more complicated signals.