Model specific parameters of VAE-IACGAN.

Fault detection in high-speed train wheelset bearings is paramount for ensuring operational safety. However, the scarcity of fault samples limits the accuracy of traditional detection methods. To address this challenge, this paper proposes a supervised generative model that integrates an Improved Auxiliary Classifier Generative Adversarial Network (IACGAN) with a Variational Auto-Encoder (VAE). Firstly, the method employs the VAE as the generator, introducing latent variables with prior information to optimize the encoding and generation process; Secondly, an independent classifier network is integrated into the ACGAN framework to enhance compatibility between classification and discriminative capabilities. Concurrently, a loss function incorporating Wasserstein distance and gradient penalty terms is designed to prevent gradient vanishing during training while satisfying Lipschitz constraints, thereby improving model stability. Experiments conducted on the XJTU bearing dataset validate that samples generated by the proposed method demonstrate superior quality assessment at both the data and feature levels compared to several GAN variants. Furthermore, the constructed VAE-IACGAN-CNN detection model achieves an average classification accuracy of 88.04%, representing a maximum improvement of 15.17% over comparative methods. This significantly mitigates accuracy degradation caused by sample imbalance, demonstrating the proposed approach’s efficacy in resolving low fault detection accuracy stemming from imbalanced high-speed train wheelset bearing samples.

高速列车轮对轴承的故障检测对于保障运营安全至关重要。然而，故障样本的稀缺性制约了传统检测方法的检测精度。为应对这一挑战，本文提出了一种融合改进型辅助分类器生成对抗网络（Improved Auxiliary Classifier Generative Adversarial Network，IACGAN）与变分自编码器（Variational Auto-Encoder，VAE）的监督式生成模型。其一，该方法以VAE作为生成器，引入携带先验信息的隐变量以优化编码与生成流程；其二，将独立分类器网络嵌入ACGAN框架，以提升分类能力与判别能力之间的兼容性。同时，本文设计了融合Wasserstein距离与梯度惩罚项的损失函数，在满足李普希茨约束的同时避免训练过程中出现梯度消失问题，进而提升模型稳定性。在XJTU轴承数据集上开展的实验表明，与多种生成对抗网络变体相比，本文方法生成的样本在数据层面与特征层面均展现出更优的质量评估效果。此外，所构建的VAE-IACGAN-CNN检测模型的平均分类准确率达到88.04%，相较于对比方法最高提升15.17%。该结果有效缓解了样本不平衡导致的准确率下降问题，证明了所提方法可有效解决因高速列车轮对轴承样本不平衡引发的故障检测精度低下的难题。