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 paper introduces the design principle of legibility as means to examine the epistemic and ethical conditions of sensing technologies. Emerging sensing technologies create new possibilities regarding what to measure, as well as how to analyze, interpret, and communicate said measurements. In doing so, they create ethical challenges for designers to navigate, specifically how the interpretation and communication of complex data affect moral values such as (user) autonomy. Contemporary sensing technologies require layers of mediation and exposition to render what they sense as intelligible and constructive to the end user, which is a value-laden design act. Legibility is positioned as both an evaluative lens and a design criterion, making it complimentary to existing frameworks such as value sensitive design. To concretize the notion of legibility, and understand how it could be utilized in both evaluative and anticipatory contexts, the case study of a vest embedded with sensors and an accompanying app for patients with chronic obstructive pulmonary disease is analyzed.
