USA As Represented by Secretary of the Navy

Kelly McCool

David J Haas

Gene L Barndt

An algorithm housed within a rotorcraft, controls real time generation of signals reflecting sensor measurement of plural parameters in accordance with a programmed sequence operationally intermapped pursuant to a linear or non-linear equation with calculations therefrom of estimates to provide an accurate determination of flight usage from which to evaluate and detect excessive damage with respect to components of the aircraft.

Monitoring of aircraft usage

Input parameters which correspond to operational flight data of an aircraft within a predetermined flight domain, are defined through measured variable state parameters generated during aircraft flight, utilizing a neural network trained by exemplars corresponding to such variable state parameters and reference information on the aircraft for data processing of real time values of the variable state parameter measurement to calculate values of the input parameters and provide a corresponding output as a reliable estimate of aircraft flight data such as airspeed, sideslip and angle of attack.

Neural network system for estimation of aircraft flight data

The invention is directed to a helicopter health and usage monitoring system utilizing a neural network for estimating gross weight and center of gravity location from measured flight condition parameter inputs; and includes means for measuring a plurality of variable flight condition parameters during flight of the helicopter; memory means for successively receiving and storing parameter input signals as well as estimates of gross weight and center of gravity location; and processing means responsive to the signals received from the measurement means for generating the gross weight and center of gravity location estimates.

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Upper Marlboro, MD, United States of America

Kelly McCool