Recent advancements in deep learning for aircraft engine fault detection have been predominantly focused on research using simulated datasets. Despite significant progress, the gap between simulated and real-world data underscores a pressing need for models that are more applicable and adaptable to the aerospace industry. This discrepancy stems from factors such as water washes, maintenance activities, noise, and nuanced variations in operating conditions. Further complicating this issue is the lack of failure data leading to class imbalance and limiting the performance of fault classification models. In response to these challenges, this study uses Generative Adversarial Networks (GANs) to augment real-world failure data from General Electric Next Generation (GEnx) aircraft engines. New synthetic data are generated using a Wasserstein GAN with Gradient Penalty (WGAN-GP) and convolutional layers. Evaluation of GAN-generated data remains an active area of research. Accordingly, we also introduce a novel validation method based on a GEnx Gas Path Analysis model. This evaluation step revealed that the GAN could effectively generate gas path response variables that were physically meaningful and consistent with the operating conditions. Furthermore, integrating the GAN-generated data into the original dataset improved the baseline fault detection model’s F1-score by an average of 2.8%. This research also highlights the GAN’s ability to learn and reproduce degradation patterns applicable across different engine units, emphasizing its potential to overcome the challenges between engine unit-to-unit variations. Additionally, this work can potentially be extended to other engine families that require synthetic data to improve maintenance strategies.