A Method for Diagnosing Engine Rotor Faults Based on Pre-trained Transformers and Generative Adversarial Networks

Ensuring the safety of aircraft engines during flight operations and mission execution is a matter of paramount importance. While deep learning-based approaches have shown promise in fault diagnosis, their effectiveness typically depends on the availability of large-scale and well-balanced labeled datasets. In real-world aviation scenarios, however, fault samples are often scarce and difficult to acquire, resulting in a significant class imbalance that degrades diagnostic performance. To better address the aforementioned challenges, this paper proposes an algorithmic framework for engine rotor fault diagnosis based on pre-trained transformers and generative adversarial networks, termed PT-Trans-WGAN-GP. The approach integrates selected layers of a pre-trained Transformer model into the discriminator and auxiliary classifier of a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). This configuration guides the generator to produce high-quality synthetic samples for under-represented fault categories, thereby augmenting and rebalancing the training dataset. A diagnostic classifier is subsequently trained on the enhanced dataset to achieve accurate identification of multiple engine fault types. By effectively addressing the data imbalance issue, the proposed method yields a significant improvement in the performance of fault diagnosis under few-shot conditions, as evidenced by the experimental findings. The framework offers a promising new direction for intelligent health management in aircraft engines.