基于飞行参数获取外场飞机疲劳关键结构载荷历程是结构故障预测与健康管理系统的一项关键技术，如何构建鲁棒性好且精度高的结构载荷回归模型是保证载荷识别和寿命预测准确性的关键。结合多元线性回归分析，提出并详细介绍了一种筛选最优输入参数组合的技术途径：首先，进行多重共线性诊断以减弱自变量之间的多重共线性；然后，进行残差分析，删除异常样本点；最后，采用逐步回归法筛选出最优自变量组合。指出传统的多重共线性诊断方法存在一定弊端，并分别基于偏相关系数法和辅助回归方程法，提出了2种更为合理可行的诊断方法。以典型战斗机翼身连接框某关键部位的载荷和应力为样本数据，对最优输入参数组合的筛选步骤展开详细说明，验证了所提出的技术途径不仅可以确保飞机结构载荷模型的精度，而且可以提高模型的稳定性和鲁棒性。
In-service structural load monitoring at critical locations based on flight data is a key technique for structural prognostic and health management system of the aircraft, and a robust and precise load model is important for load monitoring and fatigue life prediction of the aircraft. Based on multi-linear regression analysis, an approach for synthetically screening the optimal combination of input variables is presented and introduced in detail. First, the multi-collinearity diagnosis should be conducted to attenuate the multi-collinearity between independent variables. Then, residual analysis is carried out to remove abnormal observations. Finally, stepwise regression is used to find the best combination of independent variables. Traditional multi-collinearity diagnosis methods have some defects, so two more feasible methods are put forward to reduce multi-collinearity based on partial correlation coefficient method and auxiliary regression equation method, respectively. As a case study, the load and stress data at a critical wing attachment bulkhead location of a typical fighter are used to illustrate the procedures of screening the optimal combination of input variables. It is proved that the approach can not only ensure the accuracy of aircraft structural load model, but also improve the stability and robustness of the model.
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