Abstract

In the field of fault diagnosis for rotating machinery, where available fault data are limited, numerous studies have employed a generative adversarial network (GAN) for data generation. However, the limited fault data for training GAN exacerbate GAN’s inherent training instability and mode collapse issues, which are induced by adversarial training. Moreover, the stochastic nature of random sampling for latent vectors sampling often results in low-fidelity and poor diversity generation, which negatively affects the fault diagnosis models. To address these issues, this paper presents two novel approaches: a spectrum-guided GAN (SGAN) and density-directionality sampling (DDS). SGAN mitigates training instability and mode collapse through combinatorial data utilization, adversarial spectral loss, and a tailored model structure. DDS ensures the high-fidelity and high-diversity of the generated data by selectively sampling the latent vectors through two steps: density-based filtering and directionality-based sampling in the feature space. Validation on both rotor and rolling element bearing datasets demonstrates that SGAN-DDS considerably improves classification results under the limited fault data. Furthermore, fidelity and diversity analyses are conducted to validate DDS, which increase the credibility of the proposed method; and offer advancement toward the application of deep-learning and GAN in industrial fields.