Novel Methods To Estimate Rotorcraft Weight And Center Of Gravity For General Flight Conditions Using HUMS Data

An accurate, automated assessment of helicopters' gross weight (GW) and center-of-gravity (CG) is critical for the determination of aircraft fatigue and life estimates since GW/CG greatly affect static and dynamic characteristics of helicopters. Current methods of GW and CG data gathering requires not only meticulous bookkeeping but it also makes several assumptions about fuel burn rate if fuel quantity instrumentation is not available. In addition, if cargos are released/picked-up during a flight, a log of loads and time instants is required to ensure aircraftGWand CG are known at all times during the flight. This paper reviews current state of the art for the GW and CG estimations', summarizing different techniques and underlining their advantages and limitations. Preliminary results are presented in order to underline the capabilities of the proposed techniques. Then, the general framework of an innovative, hybrid approach that combines Artificial Neural Networks (ANN) and Kalman Filter (KF) techniques is introduced.