Load Estimation of Aircraft Landing Gears Using Fiber Bragg Grating Sensors

Landing gear operational load monitoring is important for the structural health monitoring of an aircraft. This paper discusses an improved method for aircraft landing gear load estimation using a minimal number of fiber Bragg grating sensors. The critical locations on the landing gear for load monitoring are identified using simulation. A machine learning model, Gaussian process regression, is used to estimate the loads at various locations on the landing gear from measured discrete strain data and common aircraft parameters. The proposed model is experimentally verified using a landing gear drop test. The results show that the estimated loads match well with those measured loads.