面向飞机辅助动力装置在翼剩余寿命预测的性能参数扩增方法

The dimension of on-wing performance parameters of the aircraft auxiliary power unit (APU) is low, it is difficult to obtain high accuracy fault prognostics result. To solve this problem, a performance parameter augment method is proposed, which is based on the generative adversarial networks (GAN). Firstly, the principle of GAN is studied, based on which the optimization parameters of the generator and discriminator are determined through the grid search algorithm. Then, the augment method facing to APU performance degradation parameter is studied, which provides the input parameters for remaining useful life (RUL) prediction of APU. Finally, the proposed method was verified and evaluated by utilizing the real on-wing monitoring data of APU from China Southern Airlines fleet. The generated 10 D exhaust temperature parameters based on GAN were processed with Euclidean distance, Pearson correlation coefficient and Kullback-Leibler divergence methods, the results show that the generated data and original data have good consistency. In the comparison experiments based on the three RUL prediction methods, the generated data and original data are both utilized for the RUL prediction, the prediction result accuracies characterized with the mean absolute error and root mean square error are improved by 8.55% and 3.62% at least compared with those using only the original performance parameters for the RUL prediction.