华东师范大学学报（自然科学版） >

2022 , Vol. 2022 >Issue 5: 219 - 232

DOI: https://doi.org/10.3969/j.issn.1000-5641.2022.05.018

物流时空数据分析与智能优化理论

基于Transformer的多特征融合的航空发动机剩余使用寿命预测

  • 马依琳 ,
  • 陶慧玲 ,
  • 董启文 ,
  • 王晔
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  • 华东师范大学 数据科学与工程学院, 上海　200062

收稿日期: 2022-07-10

  网络出版日期: 2022-09-26

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Prediction of remaining useful life of aeroengines based on the Transformer with multi-feature fusion

  • Yilin MA ,
  • Huiling TAO ,
  • Qiwen DONG ,
  • Ye WANG
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  • School of Data Science and Engineering, East China Normal University, Shanghai　200062, China

Received date: 2022-07-10

  Online published: 2022-09-26

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摘要

发动机作为飞机的核心部件, 对飞机运行起着至关重要的作用. 对航空发动机做准确的剩余使用寿命预测, 能够提前进行维护诊断, 预防重大事故的发生, 节约维护成本. 针对现有的方法缺乏对不同时间步长的考虑以及不同传感器和操作条件之间关系的研究, 提出了一种基于Transformer的多编码器特征输出融合的航空发动机剩余使用寿命预测方法. 该方法选取两个不同时间长度的输入数据, 使用排列熵对传感器之间的关系进行分析, 并将操作条件数据独立提取特征. 在广泛使用的航空发动机CMAPSS(Commercial Modular Aero-Propulsion System Simulation)数据集上进行了实验验证. 实验结果表明, 该方法优于现有的先进预测方法, 可有效提高预测精度.

关键词： 深度学习; 剩余使用寿命; 航空发动机; Transformer

本文引用格式

马依琳 , 陶慧玲 , 董启文 , 王晔 . 基于Transformer的多特征融合的航空发动机剩余使用寿命预测[J]. 华东师范大学学报（自然科学版）, 2022 , 2022(5) : 219 -232 . DOI: 10.3969/j.issn.1000-5641.2022.05.018

Abstract

As the core components of aircraft, engines play a vital role during flight. Accurate prediction of the remaining useful life of the aeroengine can help prognostics and health management, thus preventing major accidents and saving maintenance costs. In view of the lack of consideration of different time steps and the relationship between different sensors and operating conditions in existing methods, a remaining useful life prediction method based on the Transformer was proposed, which fuses multi-feature outputs from different encoder layers. This method selects two input data with different time steps, analyzes the relationship between the sensors using permutation entropy, and extracts features independently from the operating condition data. The experimental results on the public aeroengine dataset CMAPSS (Commercial Modular Aero-Propulsion System Simulation) show that the proposed method is superior to other advanced remaining useful life prediction methods.

Key words： deep learning; remaining useful life; aeroengine; Transformer

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