双通道深度卷积神经网络的航空发动机剩余使用寿命预测方法

提出了一种基于双通道的深度卷积神经网络方法，用来预测航空发动机剩余使用寿命。该方法在传统卷积神经网络上，应用最大信息系数进行数据降维、卡尔曼滤波进行数据降噪；通过数据切片，将数据片标签设置为最后一个循环的剩余使用寿命，实现数据重构；引入分段和线性剩余使用寿命衰减模型，并给出了寿命衰减起始点判断方法；将寿命衰减前、寿命衰减中2种特征作为双通道网络模型的输入。在NASA涡轮风扇发动机仿真数据集（CMAPSS）上测试结果显示，在测试数据范围较大时，该方法相关指标明显优于其他方法，在航空发动机剩余寿命预测上具有显著优势。

A Method of Predicting Aero Engines Remaining Useful Life Based on Two Channel Deep Convolutional Neural Network

A two channel deep convolutional neural network based method is proposed to predict the remaining useful life of aero engines. The method is to utilize maximal information coefficient for reducing data dimensionality noise, and Kalman filter for reducing data noise on the traditional convolution neural network. The realization of data reconstruction is subjected by data slicing and setting the data slice label as the remaining useful life of the last cycle. The segmented and linear remaining useful life decay models are introduced and a method to judge the starting point of life decay is given. The two features before and during life decay as the input of the two channel network model are used. The results of testing on NASA turbofan Engine Simulation Data Set (C MAPSS) show that the relevant indexes of this method are significantly better than that of other algorithms when the test data range is large. And this method has significant advantages in the remaining useful life prediction of aero engines.