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基于改进LSTM的航空发动机寿命预测方法研究
引用本文:郭晓静,殷宇萱,贠玉晶. 基于改进LSTM的航空发动机寿命预测方法研究[J]. 机床与液压, 2022, 50(20): 185-193
作者姓名:郭晓静  殷宇萱  贠玉晶
作者单位:中国民航大学电子信息与自动化学院
基金项目:中央高校基本科研基金项目(3122020026)
摘    要:发动机剩余寿命(RUL)预测时,进行数据特征提取易导致预测效率低下。为解决此问题,提出一种改进的长短期记忆(LSTM)算法模型。通过引入深度稀疏自动编码器(SDAE)完成时序数据的处理与特征提取,优化LSTM模型,改善航空发动机RUL预测效果。利用SDAE进行特征提取,构建健康因子(HI)曲线;同时考虑运行工况、故障模式和传感器3个因素,并分别训练其权重。利用LSTM模型进行发动机剩余寿命预测。利用涡扇发动机退化过程数据集C-MAPSS开展实验,并与DNN、BiLSTM、单层LSTM进行对比分析。结果表明:与上述3种算法相比,改进后算法的均方根误差和〖JP2〗评分函数值至少分别降低6.6%和39.1%;该方法寿命预测结果和实际寿命曲线拟合度高,验证了该方法的可行性和有效性。

关 键 词:航空发动机  长短期记忆网络  自动编码器  剩余寿命预测

Aeroengine Life Prediction Method Based on Improved LSTM
GUO Xiaojing,YIN Yuxuan,YUN Yujing. Aeroengine Life Prediction Method Based on Improved LSTM[J]. Machine Tool & Hydraulics, 2022, 50(20): 185-193
Authors:GUO Xiaojing  YIN Yuxuan  YUN Yujing
Abstract:In the prediction of engine remaining life (RUL), the data feature extraction is easy to lead to low prediction efficiency. To solve this problem, an improved algorithm model of long short-term memory (LSTM) was proposed. The sparse deep autoencoder (SDAE) method was introduced to process the time series data and optimize the LSTM model, and the RUL prediction effect of aeroengine was improved. SDAE was used for feature extraction, and the health indicator (HI) curve was constructed; three factors including operating condition, fault mode and sensor were all considered, and their weights were trained respectively. LSTM model was used to predict the remaining life. The turbo-fan degradation process data set C-MAPSS was used to carry out experiments, and compared with DNN, BILSTM and single-layer LSTM. The results show that compared with the above three alogrithms,the RMSE error and the scoring function value of the improved algorithm are decreased by at least 6.6% and 39.1% respectively; the life prediction results of this method and the actual life curve have a high fitting degree, and the feasibility and effectiveness of this method are verified.
Keywords:Aircraft engine   Long-short term memory network   Autoencoder   Remaining useful life
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