J V G Prakasa Rao Pyla 2 1 ( January - March ) In F1 vehicles Aeroelastic wing flutter is a serious concern on the integrity of the wing structure that can cause catastrophic failure in the vehicle within milliseconds of the onset of flutter at race speeds above 300 kmh The current telemetryapproaches based on convolutional neural networks CNNs or long shortterm memory LSTM models show limited predictions lead time and high falsealarm rates under the highly dynamic aeromechanical conditions they encounter when handling race events This paper introduces the Wing Flutter Detection TFWFD framework based on Transformer combining highspeed vision and multiple sets of aeroelastic parameters from inertial measurement units IMUs and fibre Bragg grating FBG strain gauge dataThis paper proposes a framework of Wing Flutter Detection TFWFD based on Transformer which integrates heterogeneous multisensor telemetry data including a highspeed vision system IMUs and fibre Bragg grating FBG strain meter A realtime risk metric based on the structural deformation acceleration and velocity features is derived from a composite Flutter Index FI which is interpretable Understanding how well the framework can perform it achieves an accuracy of 979 in classification an F1score of 0974 in detection a detection lead time of 180ms twice that of CNN baselines and 005 false alarms per hour which allows for proactive aerodynamic intervention before structural damage is imminent Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026