Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.
This qualitative study examined how the complex institutional context of gas extraction in Groningen affects relations and processes of trust, and seeks to better understand what is necessary for restoring trust. In the Groningen gas case, responsibilities for dealing with multiple negative consequences of gas extraction are shared by many different organizations who together form a complex institutional system. Numerous professionals are doing their best to help solve the problems. As individuals, case managers and other professionals are seen as benevolent and hard-working people. But as representatives of (large) institutions these professionals struggle to be seen as trustworthy because of persistent problems with institutional performance, with professionals themselves feeling they have insufficient discretionary power. More than interpersonal trust, a different form of trust appears to be at stake here: confidence in the system itself. According to many respondents, confidence in the system is low because the perceived interests of the institutions that shaped this system are not aligned with those of residents and the region. In addition, the positions of power and responsibility within this system are opaque to both residents and professionals. Moreover, the institutional system is perceived to be based on a distrustful attitude toward citizens in general, resulting in elaborate procedures for accountability, control and monitoring. These factors have become obstacles to restoring confidence in the system, no matter how well residents and professionals get along as individuals.
Trustworthy data-driven prognostics in gas turbine engines are crucial for safety, cost-efficiency, and sustainability. Accurate predictions depend on data quality, model accuracy, uncertainty estimation, and practical implementation. This work discusses data quality attributes to build trust using anonymized real-world engine data, focusing on traceability, completeness, and representativeness. A significant challenge is handling missing data, which introduces bias and affects training and predictions. The study compares the accuracy of predictions using Exhaust Gas Temperature (EGT) margin, a key health indicator, by keeping missing values, using KNN-imputation, and employing a Generalized Additive Model (GAM). Preliminary results indicate that while KNN-imputation can be useful for identifying general trends, it may not be as effective for specific predictions compared to GAM, which considers the context of missing data. The choice of method depends on the study’s objective: broad trend forecasting or specific event prediction, each requiring different approaches to manage missing data.
