  Generative Adversarial Networks (GANs) for producing realistic signals, have
substantially improved fault diagnosis algorithms in various Internet of Things
(IoT) systems. However, challenges such as training instability and dynamical
inaccuracy limit their utility in high-speed rail (HSR) bogie fault diagnosis.
To address these challenges, we introduce the Pulse Voltage-Guided Conditional
Diffusion Model (VGCDM). Unlike traditional implicit GANs, VGCDM adopts a
sequential U-Net architecture, facilitating multi-phase denoising diffusion for
generation, which bolsters training stability and mitigate convergence issues.
VGCDM also incorporates control pulse voltage by cross-attention mechanism to
ensure the alignment of vibration with voltage signals, enhancing the
Conditional Diffusion Model's progressive controlablity. Consequently, solely
straightforward sampling of control voltages, ensuring the efficient
transformation from Gaussian Noise to vibration signals. This adaptability
remains robust even in scenarios with time-varying speeds. To validate the
effectiveness, we conducted two case studies using SQ dataset and
high-simulation HSR bogie dataset. The results of our experiments unequivocally
confirm that VGCDM outperforms other generative models, achieving the best
RSME, PSNR, and FSCS, showing its superiority in conditional HSR bogie
vibration signal generation. For access, our code is available at
https://github.com/xuanliu2000/VGCDM.
