针对基于应变测量的载荷分布反演，首先对比分析现有几种影响系数法，并讨论影响载荷反演精度的几个关键问题。为解决单位载荷工况选取的难题，提出一种基于施密特正交化的最大垂直距离逐步筛选基工况法，从设计载荷工况库中筛选载荷基工况和应变基工况，建立一套完整的工程可行的载荷分布反演流程。为解决反向矩阵病态化的难题，引入Tikhonov正则化方法。以某型飞机机翼疲劳试验作动筒载荷和光纤传感器数据为算例，假设3种应变测量误差情况（无误差、5%随机误差、10%随机误差），验证了提出的载荷分布反演方法具有很高的预测精度和鲁棒性，可为新一代航空航天器的结构健康监控提供一条可靠的载荷识别途径。
Aiming at the inverse problem of distributed load recovery from strain measurements, we first compare several existing influence coefficient methods and discuss the key problems affecting the load inversion accuracy. To solve the selection problem of unit load cases, a stepwise method based on Schmidt's orthogonalization of the maximum vertical distance is proposed to sequentially select the basis cases from the design load case library, establishing a complete set of engineering feasible distributed load recovery approach. Meanwhile, the Tikhonov regularization method is introduced to solve the ill-conditioning of the inverse matrix. Finally, left wing load rams and optical fiber sensor data of a certain aircraft full-scale fatigue test are adopted for a case study, with 0, 5% and 10% random strain measurement errors introduced, respectively. It is verified that the inverse approach has high prediction accuracy and robustness and is reliable for the flight distributed load identification of structure health monitoring of the new generation aerospace vehicles.
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