Rolling bearing is the key supporting component of aero-engines, of which fault diagnosis is very important to ensure its reliable operation and continuous airworthiness. However, the data imbalance problem caused by its complex and harsh environment restricts the intelligent diagnosis. This paper proposes a sample enhanced diagnostic method based on pre-training and auxiliary classifier Wasserstein generative adversarial network with gradient penalty (PT-WGAN-GP)