Prototype-Driven Moment Exchange for Data Augmentation in High-Speed Train Fault Diagnosis

Reliable fault diagnosis in high-speed train systems remains challenging due to complex operating conditions and limited labeled fault data. To address this issue, we propose Prototype-Mixed Moment Exchange (PMoEx), a feature-space data augmentation method that enhances sample diversity by exchanging statistical moments-mean and variance-between samples, modulated by class-level prototypes. Unlike existing approaches such as mixup and MoEx, PMoEx explicitly integrates class-conditioned statistics, enabling the preservation of discriminative features while mitigating inter-class interference. Experimental results on a simulated high-speed train fault dataset demonstrate that PMoEx consistently improves diagnostic performance under data-scarce conditions, outperforming conventional augmentation methods.