Intelligent Fault Diagnosis of Unbalanced Samples Using Optimized Generative Adversarial Network

Abstract

The increasing range of faults encountered by mechanical systems has brought great challenges for conducting intelligent fault diagnosis based on insufficient samples, in recent years. To tackle the issue of unbalanced samples, an improved methodology based on a generative adversarial network that uses sample generation and classification is proposed. First, 1D vibration signals are transformed into 2D images considering the features of the vibrating signals. Next, the optimized generation adversarial network is constructed for adversarial training to synthesize diverse fake 2D images according to actual sample characteristics with the generative model as a generator and the discriminative model as a discriminator. Our model uses an attenuated learning rate with a cross-iteration batch normalization layer to enhance the validity of the generator. Last, the discriminative model as a classifier is used to identify the fault states. The experimental results demonstrate that the proposed strategy efficiently improves fault identification accuracy in the two cases of sample imbalance.