This paper presents a novel approach of principal component analysis- (PCA-) assisted back propagation (BP) neural networks for the problem of rotor blade load prediction. 86.5 hours of real flight data were collected from many steady-state and transient flight maneuvers at different altitudes and airspeeds. Prediction of the blade loads was determined by the PCA-BP model from 16 flight parameters measured and monitored by the flight control computer already present in the helicopter. PCA was applied to reduce the dimension of the flight parameters influencing the component load and eliminate the correlation among flight parameters. Thus, obtained principal components were used as input vectors of the BP neural network. The combined PCA-BP neural network model was trained and tested by real flight data. Comparison of this model and to a BP neural network model as well as to a multiple linear regression (MLR) model was also done. The results of comparison demonstrate that the PCA-BP model has higher prediction precision with an average error of 2.46%, while 4.49% for BP and 10.20% for MLR. The results also reveal that the PCA-BP model has a shorter convergence path than the BP model. This method not only is useful in establishing the load spectra of helicopter rotor in-service where installation of strain gauges is impractical but also can reduce the cost of installation and maintenance measured by strain gauges.

中文翻译：

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主成分分析辅助神经网络在转子叶片载荷预测中的应用

本文针对转子叶片载荷预测问题提出了一种新的主​​成分分析（PCA）辅助反向传播（BP）神经网络方法。在不同的高度和空速下，从许多稳态和瞬态飞行演习中收集了86.5小时的真实飞行数据。通过PCA-BP模型从直升机中已经存在的飞行控制计算机测量和监视的16个飞行参数中确定叶片载荷的预测。应用PCA来减小影响零件载荷的飞行参数的尺寸，并消除飞行参数之间的相关性。因此，将获得的主成分用作BP神经网络的输入向量。通过实际飞行数据对组合的PCA-BP神经网络模型进行了训练和测试。还对该模型与BP神经网络模型以及多元线性回归（MLR）模型进行了比较。比较结果表明，PCA-BP模型具有较高的预测精度，平均误差为2.46％，而BP的平均误差为4.49％，MLR的平均误差为10.20％。结果还表明，PCA-BP模型的收敛路径比BP模型短。该方法不仅可用于建立无法安装应变片的直升飞机旋翼的载荷谱，而且还可降低由应变片测量的安装和维护成本。对于MLR，为20％。结果还表明，PCA-BP模型的收敛路径比BP模型短。该方法不仅可用于建立无法安装应变片的直升飞机旋翼的载荷谱，而且还可降低由应变片测量的安装和维护成本。对于MLR，为20％。结果还表明，PCA-BP模型的收敛路径比BP模型短。该方法不仅可用于建立无法安装应变片的直升飞机旋翼的载荷谱，而且还可降低由应变片测量的安装和维护成本。