Despite rapid progress in intelligent gear fault diagnosis, it still faces two bottlenecks, namely the scarcity of labeled fault data and the computational burden of digital-twin-based approaches. To address this, we propose a physics-informed generative adversarial network (PIGAN) that integrates physical priors with data-driven learning. A Fourier feature mapping layer encodes multi-scale frequency content to better capture gear-meshing harmonics and transient impacts, while a physics-informed neural network imposes a gear dynamic model as a hard constraint to enforce governing laws. An adversarial framework further learns latent distributions from a few samples, thereby enhancing generalization and sample diversity. Experiments on a two-stage gear transmission system show that PIGAN generates physically consistent vibration data with high time- and frequency-domain fidelity, proving the effectiveness of the proposed method.

Data augmentation; GAN; PINN; Gear dynamic model