基于LSTM分类器的航空发动机预测性维护模型

利用传感器数据进行预测性维护是航空发动机故障预测与健康管理(prognostic and health manage-ment,PHM)的关键问题.针对发动机剩余寿命预测准确性低的问题,提出基于长短期记忆网络(long short-term memory network,LSTM)分类器的预测性维护模型.LSTM分类器...

Predictive maintenance model of aeroengine based on LSTM classifier

Predictive maintenance using sensor data is a key issue in aeroengine prognostic and health management(PHM). Aiming at the problem of low accuracy of remaining useful life prediction of aeroengine, a predictive maintenance model based on long short-term memory network(LSTM) classifier is proposed. The LSTM classifier fully screens the long time sequence information through the gating unit, and uses the effective information for time sequence prediction. Firstly, a sliding time window is used to prepare training samples. Secondly, the pre-processed samples are input into the LSTM to predict the failure probability of the equipment in a specific time window. Then, by adjusting the window size, a two-class model with the best performance is obtained to better adapt to predictive maintenance requirements. Finally, the National Aeronautics and Space Administration C-MAPSS data set is used to verify the effectiveness of the model. Compared with the existing classification methods, the proposed model is more accurate in rumaining useful life classification.