Remaining Useful Life Prediction for Turbofan Engine using SAE-TCN Model
作者单位：School of Transportation Science and EngineeringBeihang University
会议名称：《第40届中国控制会议》
会议日期：2021年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 082503[工学-航空宇航制造工程] 0835[工学-软件工程] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Deep Learning Turbofan Engine Remaining Useful Life Prediction Temporal Convolutional Network Autoencoder Target Generation
摘 要：Turbofan engines are known as the heart of the aircraft,as important equipment of the aircraft,the health state of the engine determines the aircraft’s operational ***,the equipment monitoring and maintenance of the engine is an important part of ensuring the healthy and stable operation of the aircraft,and the remaining useful life（RUL） prediction of the engine is an important part of *** monitoring data of turbofan engines have a high dimension and a long time span,which brings difficulties to predicting the remaining useful life of the *** paper proposes a residual life prediction model based on Autoencoder and temporal convolutional network（TCN）.Among them,Autoencoder is used to reduce the dimension of the data and extract features from the engine monitoring *** obtained low-dimensional data is trained in the TCN network to predict the remaining useful *** model mentioned in this article is verified on the NASA public dataset（C-MAPSS）and compared with common machine learning methods and other deep neural *** experimental results show that the model proposed in this paper performs best in the evaluation methods,and this conclusion has important implications for engine health.