Improved Generative Adversarial Network for Rotating Component Fault Diagnosis in Scenarios With Extremely Limited Data

Traditional data-driven intelligent fault diagnosis methods for rotating component commonly assume that sufficient labeled data is available. However, the rotary machine works in a normal state most of the time in practical engineering, resulting in fault diagnosis scenarios with extremely limited and imbalanced data. This phenomenon deteriorates the performance of intelligent fault diagnosis methods. In this article, a data augmentation method based on an improved variational autoencoding generative adversarial network is proposed to address this problem. First, the self-attention mechanism and Wasserstein distance with gradient penalty are introduced to improve data generation capability and training stability of the generative model. Then, the generative models are used to generate synthetic samples with high similarity and diversity based on extremely limited frequency spectrum samples. Subsequently, data filters are established to automatically refine the generated samples. Finally, the refined samples are used to enrich imbalanced dataset and improve the fault diagnosis accuracy. The effectiveness of the proposed method is validated by vibration datasets of two rotating components. Experimental results show that the proposed method can generate high-quality samples and has the potential to enhance the fault diagnosis performance in scenarios with limited data.