关键词: 航空发动机;在线预测模型;滑动时窗策略;遗传算法;支持向量机

Abstract: In order to improve the traditional time series prediction method of aeroengine performance parameters, an adaptively optimized Support Vector Machine (SVM) online prediction model based on sliding time window strategy is proposed. It solves the shortcoming of poor dynamic adaptability of training samples and increases the accuracy of prediction model affected by outdated data. The training samples of time window data are updated in real time based on sliding time window strategy. The embedding dimension is determined adaptively by using the Final Prediction Error (FPE) criterion. Genetic algorithm (GA) is applied to optimize the hyperparameters of SVM prediction model adaptively in real time. The aero-engine performance parameters Delta Exhaust Gas Temperature (DEGT) data are used to verify the validity of the proposed method, and the results show that the prediction accuracy of the adaptively GA-optimized SVM (GASVM) online prediction model based on sliding time window strategy is significantly better than that of the traditional GASVM prediction model. The influence of different time window widths on short-term prediction accuracy is further analyzed. The 1~10 steps prediction results of this model are presented. The results show that the online prediction model has good and stable short-medium term (less than 5 steps) prediction result under different time window widths. The online prediction model proposed in this paper can be used to predict the performance parameters of aero-engines. It realizes the early warning of the future performance changes of aero-engines.

Key words: Aero-engine;Online prediction model;Sliding time window;Genetic algorithm;Support vector machine