Predictive maintenance anticipates and prevents component failures by analysing operational data for early signs of degradation. Traditional industry-standard models for aircraft systems are often rule-based, missing complex patterns and limiting scalability. This research develops a deep learning (DL) fault detection pipeline for the engine bleed air system of wide-body twin-engine aircraft, leveraging real-world operational sensor and maintenance data. An interpretable feature engineering framework extracts physically informed features, including dual-engine comparisons, to train a gated recurrent unit (GRU) fault detection model for robust temporal modelling of healthy and faulty conditions. Bayesian optimisation is implemented for hyperparameter tuning. However, the scarcity of representative failure data, which is a common issue in aviation, limits the achievable performance and fidelity of DL models. To address this, a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is employed to augment the dataset with synthetic failure data. A post-processing block labeling technique is introduced to enhance fidelity, and a novel fidelity savings metric translates model predictions into operational savings. GAN-based augmentation enhances recall, precision, and F1-score, and outperforms traditional augmentation. Further case study results show that the WGAN-GP-augmented model delivers four times the operational savings compared to the industry-standard model, 50\% more than the non-augmented GRU, and 31\% more than traditional augmentation.