飞机性能参数预测的不确定性处理

 利用飞机的性能参数对飞机进行故障预报和状态监控是非常重要的。飞机的性能参数不仅具有非线性而且往往包含噪声,使得故障预测结果具有不确定性。针对这些问题,研究了利用非线性支持向量机处理飞机性能参数的预测问题,通过增加线性约束的方式解决了噪声带来的不确定性问题。此种方法不仅提高了预测的精度,而且模型可以利用适用于处理大规模二次规划的序列最小最优化算法进行求解,使得其可以解决大数据量的预测问题。利用仿真数据以及实际飞机性能参数对该方法进行了实验分析,实验结果表明此方法在精度上较不考虑噪声影响的模型有所提高,对于进一步提高飞机故障预测的精度,从而提高飞机的安全性具有重要意义。

Uncertainty Analysis of Aircraft Flight Parameters Prediction

Flight performance parameters can be used for fault prediction and condition monitoring,which is of great importance for the improvement of flight security and reduction of aircraft maintenance costs.Since the airplane is a complicated system,its performance parameter series are always nonlinear.In addition,affected by the operating environment,driving factors and noises generated by sensors,the performance parameters are often mixed with noises,which leads to uncertainty in prediction results.In order to deal with this problem,a new method is proposed to predict flight parameters by using a nonlinear support vector machine.By adding a new restriction,the uncertainty problem is properly solved.This method can not only enhance prediction precision,but also deal with problems involving large amounts of input data by using sequential minimal optimization.The method is evaluated by simulation data and actual flight performance parameters.Test results show that this new model which takes noise into consideration exhibits an improvement in precision as compared with the original model.Thus,this new method provides better precision for flight malfunction prediction,which is of great significance in enhancing flight safety.