Learning structural stress virtual sensors from on-board instrumentation of a commercial aircraft

Abstract

This work aims to predict the mechanical stress on the structure of a business jet in service phase from flight instrument only. A significant database obtained from test flights using aircraft instrumented with strain gauges has been provided as part of an Artificial Intelligence challenge organized by the French Ile-de-France region and the aircraft manufacturer Dassault Aviation. Learning techniques are considered to train a prediction model of the aircraft structural stress. The proposed baseline includes a clustering step for phase identification in time series and an ensemble model with two stacked regressors. The model is trained on a dataset of 117 flights and its overall performance is evaluated on a validation set of flight sequences from 186 flights. The main advantages of the proposed learning approach are its prediction accuracy, its training frugality and its interpretability. This paper presents a global data science workflow applied to a problem of structural stress prediction. Despite a development in a constrained time period with no direct access to the data, the proposed approach demonstrates the feasibility of the concept of learned virtual sensor of aircraft structural stress and paves the way for applications to other structures.