Establishing a flight load parameter identification model with support vector machine regression

Flight load parameter identification is crucial for individual aircraft fatigue monitoring and is achieved mainly through the transformation between flight parameters and flight loads, thus obtaining the load spectrum of a key structural component indirectly. To solve the problem of nonlinear identification of flight parameters and flight loads, we take the typical maneuver actions of an aircraft into consideration and establish an improved flight load parameter identification model with support vector machine regression (SVM-R), which we believe is effective. The core of the mathematical model consists of: (1) we use the principal component analysis to reduce the inputs of the SVM-R; (2) we use the cross-validation method and the genetic algorithm to globally search for and optimize the SVM-R model parameters; (3) we use the optimized SVM-R model parameters to train their identification model. We verify the effectiveness of our identification model by comparing the measured bending moment of a key component of an aircraft in semi-roll flight maneuver with its identified bending moment. The verification results, given in Figs. 4 and 5, and their analysis show preliminarily that the maximum relative residual value of the bending moment is 12.3858% and that the average relative residual value is 2.3688%, satisfying the requirements that the maximum relative residual value should be controlled within 20% of the measured load and that the average relative residual value should be within 3%, thus indicating that our flight load parameter identification model is accurate and effective.