Can we Fix it?

Abstract

Research aimed at improving engine maintenance practices is vital to ensuring the aviation industry's high safety standards. Both preventive and corrective maintenance approaches result in either higher costs or increased failures. Predictive maintenance aims to achieve an optimal balance by using time series and historical failure data to anticipate future problems and plan engine maintenance activities. While extensive research has been done on the application of AI models for predictive maintenance using simulated datasets, limited research has been done using real world engine data. Simulated datasets lack the complexity of real world systems which undermines the applicability of their findings to real world situations. This paper addresses the gap between theory and industry by utilizing eight years of operating and maintenance data from the GEnx-1B, provided by KLM Engine Services. We expand classic residual based methods with a novel approach, sister engine analysis, to improve the performance of an LSTM-based fault detection model. The data is labeled based on maintenance reports, which provide the most accurate link to the underlying degradation state of an engine. These reports reveal a significant bias toward high pressure turbine degradation in the GEnx-1B. Furthermore, our findings indicate that incorporating sister engine analysis enhances the average F1 score of an AI-based fault detection model by 7.7% and that AI methods outperform the current industry standard when provided with the same input data. The use of physically relevant model features and LIME analysis ensures the model's behavior is interpretable. These results are significant as they help build trust in AI-based solutions for predictive maintenance, which is crucial for their broader adoption.