Prediction of Vertical Tail Maneuver Loads Using Backpropagation Neural Networks

Abstract

Federal aviationregulationsrequire that structures critical to the safe operationofanaircraftmustnot fail within their expected lifetimes due to damage caused by the repeated loads typical to its operations. A backpropagation neural network has been used to predict maneuver-induced strains in the vertical tail spar of a Cessna 172P. Linear accelerometer, angular accelerometer, rate gyro, and strain gauge signals were collected during  ights using a portable data acquisition system for Dutch roll, roll, sideslip, level turn, and push–pull maneuvers. Sensor signals were Ž ltered and used to train the network. The strains in the vertical tail spar were predicted successfully by the network to within 50 μ of their strain gauge values. This is an inexpensive and effective technique for collecting vertical tail load spectra for small transport airplanes already in service where installation of strain gauges are impractical.