《计算机应用》唯一官方网站 ›› 2022, Vol. 42 ›› Issue (9): 2960-2968.DOI: 10.11772/j.issn.1001-9081.2021071343

• 前沿与综合应用 • 上一篇    

基于优化混合模型的航空发动机剩余寿命预测方法

刘月峰(), 张小燕, 郭威, 边浩东, 何滢婕   

  1. 内蒙古科技大学 信息工程学院,内蒙古 包头 014010
  • 收稿日期:2021-07-27 修回日期:2021-10-15 接受日期:2021-10-21 发布日期:2021-10-27 出版日期:2022-09-10
  • 通讯作者: 刘月峰
  • 作者简介:张小燕(1997—),女,内蒙古呼和浩特人,硕士研究生,主要研究方向:深度学习、航空发动机剩余寿命预测;
    郭威(1998—),男,内蒙古呼伦贝尔人,硕士研究生,主要研究方向:知识图谱;
    边浩东(1997—),男,内蒙古呼伦贝尔人,硕士研究生,主要研究方向:图像处理;
    何滢婕(1998—),女,山西太原人,硕士研究生,主要研究方向:电池荷电状态估计。
  • 基金资助:
    内蒙古纪检监察大数据实验室开放课题基金资助项目(IMDBD2020022)

Remaining useful life prediction method of aero-engine based on optimized hybrid model

Yuefeng LIU(), Xiaoyan ZHANG, Wei GUO, Haodong BIAN, Yingjie HE   

  1. School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou Inner Mongolia 014010,China
  • Received:2021-07-27 Revised:2021-10-15 Accepted:2021-10-21 Online:2021-10-27 Published:2022-09-10
  • Contact: Yuefeng LIU
  • About author:ZHANG Xiaoyan, born in 1997, M. S. candidate. Her research interests include deep learning, prediction of remaining useful life of aero-engine.
    GUO Wei, born in 1998, M. S. candidate. His research interests include knowledge graph.
    BIAN Haodong, born in 1997, M. S. candidate. His research interests include image processing.
    HE Yingjie, born in 1998, M. S. candidate. Her research interests include estimation of battery state of charge.
  • Supported by:
    Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory Open Project Fund(IMDBD2020022)

摘要:

针对航空发动机剩余使用寿命(RUL)预测方法没有同时加权不同时间步下的数据,包括原始数据和所提取的特征,导致RUL预测准确性较低的问题,提出了一种基于优化混合模型的RUL预测方法。首先,选用三种不同的路径提取特征:1)将原始数据的均值和趋势系数输入至全连接网络;2)将原始数据输入双向长短期记忆(Bi-LSTM)网络,并采用注意力机制处理得到的特征;3)使用注意力机制处理原始数据,并将加权特征输入至卷积神经网络(CNN)和Bi-LSTM网络中。然后,采用融合多路径特征预测的思想,将上述提取到的特征融合后输入至全连接网络获得RUL预测结果。最后,使用商用模块化航空推进系统仿真(C-MAPSS)数据集验证方法的有效性。实验结果显示,所提方法在4个数据集上均有较好的表现。以FD001数据集为例,所提方法的均方根误差(RMSE)比Bi-LSTM网络降低了9.01%。

关键词: 剩余使用寿命, 航空发动机, 注意力机制, 卷积神经网络, 双向长短期记忆网络

Abstract:

In the Remaining Useful Life (RUL) prediction methods of aero-engine, the data at different time steps are not weighted simultaneously, including the original data and the extracted features, which leads to the problem of low accuracy of RUL prediction.Therefore, an RUL prediction method based on optimized hybrid model was proposed. Firstly, three different paths were chosen to extract features. 1) The mean value and trend coefficient of the original data were input into the fully connected network. 2) The original data were input into Bidirectional Long Short-Term Memory (Bi-LSTM) network, and the attention mechanism was used to process the obtained features. 3) The attention mechanism was used to process the original data, and the weighted features were input into Convolutional Neural Network (CNN) and Bi-LSTM network. Then, the idea of fusing multi-path features for prediction was adopted, the above-mentioned extracted features were fused and input into the fully connected network to obtain the RUL prediction result. Finally, the Company-Modular Aero-Propulsion System Simulation (C-MAPSS) datasets were used to verify the effectiveness of the method. Experimental results show that the proposed method performs well on all the four datasets. Taking FD001 dataset as an example, the Root Mean Square Error (RMSE) of the proposed method is reduced by 9.01% compared to that of Bi-LSTM network.

Key words: Remaining Useful Life (RUL), aero-engine, attention mechanism, Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM) network

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