Augmenting Aircraft Engine Flight Data with Generative Adversarial Networks for Fault Detection

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...

Enhancing Aviation Maintenance with Explainable AI: A Bayesian Approach to Counterfactual Explanations for Remaining Useful Life Estimation

Machine learning models have improved Prognostics and Health Management (PHM) in aviation, notably in estimating the Remaining Useful Life (RUL) of aircraft engines. However, their 'black-box' nature limits transparency, critical in safety-sensitive aviation maintenance. Explainable AI (XAI), particularly Counterfactual (CF) explanations, offers...

Predictive Maintenance Decisions for a Multi-Component Aircraft based on Prognostics

Aircraft maintenance is critical to an airline's operations to ensure the reliability, availability, and safety of their assets. Recently, the approach of using component prognostics in aircraft maintenance has received increasing attention in academic- and industrial research. Predictive maintenance has demonstrated promising results in using...

Assessing the Impact of Condition-Based Maintenance as a Function of the Variation in Prognostics Performance Levels

As the profit margins of the airline industry are relatively low, it is of utmost importance to keep costs low in order for airlines to stay competitive. An important cost factor is maintenance costs, as it can take up around 10-20 % of the total direct operational costs. Currently, much development is taking place in developing condition-based...