一种可用于航空发动机健康状态预测的动态集成极端学习机模型

A dynamic ensemble extreme learning machine model for aircraft engine health condition prediction

提出一种动态集成极端学习机模型用于航空发动机健康状态预测.采用AdaBoost.RT集成学习算法对极端学习机(ELM)进行集成,在训练时采用每个训练样本的近邻样本对ELM的局域性能进行评估;在预测时首先确定新样本在训练样本集中的近邻样本,然后根据ELM在近邻样本上的性能来赋予集成权值实现弱学习机的动态集成.以燃油流量为指标进行航空发动机健康状态预测,动态集成ELM模型短期预测结果的平均相对误差绝对值(MAPE)为3.688%,小于单一ELM模型的3.830%以及静态集成ELM模型的3.719%;长期预测结果中动态集成ELM模型的MAPE为3.075%,小于单一ELM模型的4.355%以及静态集成ELM模型的3.884%.因此动态集成ELM模型更适用于航空发动机健康状态预测.

A dynamic ensemble extreme learning machine （ELM） model was proposed aircraft engine health condition prediction. The AdaBoost. RT algorithm was used to integrate ELM to construct the ensemble model. During the training process, the neighboring samples of every training sample were employed to evaluate the local performance of ELM. In the prediction process, the neighboring samples of new samples in the training sample set were selected firstly, then the combined weights of ELM were determined by the performance on the neighboring samples, implementing the dynamic ensemble of the weak learning machine. Fuel flow was utilized as a health index for aircraft engine health condition prediction. For short term prediction, the mean absolute percentage error （MAPE） of the dynamic ensemble ELM model was 3. 688 %, less than the MAPE of the single ELM model and the static ensemble ELM model, which were 3. 830% and 3. 719%, respectively. And for long term prediction, the MAPE of the dynamic ensemble ELM model was 3. 075%, also less than that of the single ELM model of 4. 355% and the static ensemble ELM model of 3. 884%. Thus, the dynamic ensemble ELM model is better for the aircraft engine health condition prediction.