Enhanced Anomaly Detection in Aero-Engines using Convolutional Transformers

Abstract

Gas turbines are vital in power generation and propulsion systems. However, these engines are exposed to complex and variable operating conditions, which makes early and accurate fault detection essential for predictive maintenance and minimizing unplanned downtime. This paper proposes a novel approach that combines convolutional neural networks (CNNs) with transformer architectures to address these challenges. The proposed Convolutional transformer model aims to enhance the accuracy and robustness of turbofan fault classification by integrating the feature extraction capabilities of CNNs with the contextual learning strengths of transformers. Through rigorous experiments, we seek to demonstrate our approach's performance in classification accuracy and generalization across different operating conditions. We utilize a comprehensive synthetic dataset, C- MAPSS, derived from multiple aircraft engine units as the benchmark for this study. The results for the proposed model show an accuracy of 99.6% on the test dataset. The outcome has the potential to be extended and fine-tuned for different types of gas turbines for diverse applications.