Data-Driven Approach to the Development of an Aeroelastic Flutter Precursor

This paper presents a novel algorithm for aeroelastic flutter early detection. Two new features for flutter onset detection are presented. Flutter early warning is accomplished using only measured signals, with essentially no prior knowledge needed about the aircraft or the flutter mechanism involved. The algorithm consists of three stages: 1) extraction of regularity features, 2) calibration by addition of white noise to nominal measurements, and 3) thresholding. Four types of datasets were used: a) synthetic data, b) simulated data generated using aeroelastic response simulations to stochastic gusts, c) measured data from a wind tunnel experiment, and d) flight test data including actual flutter onsets. The algorithm was shown to be able to flag an impending flutter event before critical onset occurs. (The Python code for paper is available at https://github.com/bmeivar/flutter.)