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 mod-el.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 .%飞行载荷参数识别是单机寿命监控中的重要技术，主要通过建立飞行参数与飞行载荷之间的转换关系，实现间接获取关键部位的载荷谱。针对飞行参数与飞行载荷之间非线性识别问题，结合飞机典型的机动动作，提出了一种改进的支持向量机回归（ SVM-R）飞行载荷识别模型。该模型首先采用主成分分析缩减SVM-R模型输入，再利用交叉验证和遗传算法优化 SVM-R模型设置参数，最后根据优化参数训练得到飞行载荷的SVM-R识别模型。通过在半滚机动动作下，飞行参数识别某一部位弯矩的实例分析，验证了优化改进的SVM-R模型对飞行载荷识别的最大残差可控制在实测载荷的20％以内，平均残差控制在实测载荷的3％以内，且优于未经优化的SVM-R模型。
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