面向航空发动机多传感器并行预测模型的设计与实现

Design and Implementation of Parallel Prediction Model for Aeroengine Multi-Sensor

为了准确预测航空发动机工作时传感器数据的变化趋势,有效监控航空发动机的工作状态,该文就发动机高压压气机转子转速、燃烧室燃油喷嘴压力、涡轮后温度等数个航空发动机主要传感器数据,使用滑动窗口算法截取子序列构建数据集并对其进行标准化。提出了一种基于Seq2Seq的面向航空发动机多传感器数据预测神经网络模型(AMSDPNN),并对该网络进行优化,最终实现了对航空发动机多传感器数据的预测。实验表明,相较于其他传统数据预测模型,该模型有着更好的预测效果,其均方误差值为0.1%,同时提前320 ms实现了对航空发动机传感器数据的预测。

In order to accurately predict the changing trend of sensor data when the aeroengine is operating,and to effectively monitor the working status of the aeroengine,the sliding window algorithm is used to intercept the subsequences to construct the time series data set and standardize them based on the data of several main aeroengine sensors:engine high pressure compressor rotor speed(N_(2)),combustion chamber fuel nozzle pressure(P_(tk)),turbine temperature(T_(t6))and so on.Then we propose a multi-sensor data prediction model of aeroengine based on Seq2Seq which is called AMSDPNN(aeroengine multi-sensor data prediction neural network)and optimizes this neural network model to realize the prediction of aeroengine multi-sensor data.The experimental results show that this model has better prediction results than other traditional data prediction models and the mean square error(MSE)is 0.1%.And the prediction of aeroengine sensor data is advanced by 320ms.