数据驱动智能故障诊断技术在液体火箭发动机中的应用与展望

  • 杨述明 ,
  • 吴建军 ,
  • 谢昌霖 ,
  • 程玉强 ,
  • 王彪
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  • 1. 国防科技大学空天科学学院
    2. 国防科技大学

收稿日期: 2024-10-21

  修回日期: 2025-02-21

  网络出版日期: 2025-02-25

Application issues of data-driven intelligent fault diagnosis technologies for liquid rocket engines

  • YANG Shu-Ming ,
  • WU Jian-Jun ,
  • XIE Chang-Lin ,
  • CHENG Yu-Qiang ,
  • WANG Biao
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Received date: 2024-10-21

  Revised date: 2025-02-21

  Online published: 2025-02-25

摘要

故障诊断是保障液体火箭发动机(Liquid Rocket Engines, 液体火箭发动机)安全性的关键技术之一,其中基于模型的诊断方法受限于诊断精度与模型准确度间不可调和的矛盾,以信号处理技术为典型代表的数据驱动诊断方法高度依赖专家领域知识。在人工智能和大数据快速发展背景下,数据驱动智能故障诊断(Data-driven Intelligent Fault Diagnosis, 数据驱动智能故障诊断)方法获得了广泛关注,并在各种工程应用中取得了巨大成功。因此,本文从机器学习的模型结构和特征工程两个维度,梳理数据驱动智能故障诊断方法在液体火箭发动机中的应用模式,进一步分析数据驱动智能故障诊断方法在液体火箭发动机实际健康监测应用中面临的三大挑战,并基于团队研究成果给出针对性解决措施,最后对数据驱动智能故障诊断方法存在的差距和下一步发展趋势进行总结。

本文引用格式

杨述明 , 吴建军 , 谢昌霖 , 程玉强 , 王彪 . 数据驱动智能故障诊断技术在液体火箭发动机中的应用与展望[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2025.31427

Abstract

Fault diagnosis is one of the key technologies to ensure the safety of liquid rocket engines. The model-based diagnostic methods are limited by the irreconcilable contradiction between diagnostic accuracy and the model accuracy, and data-driven diagnostic methods, typified by signal processing techniques, rely heavily on expert domain knowledge. With the rapid development of artificial intelligence and big data, the data-driven intelligent fault diagnosis methods have received extensive attention and achieved great success in a great variety of engineering applications. Therefore, the application modes of the data-driven intelligent fault diagnosis methods in liquid rocket engines was reviewed from the perspectives of model structures and feature engineering of machine learning. The three major challenges faced by the the data-driven intelligent fault diagnosis methods in the practical health monitoring application of liquid rocket engines were further analyzed, and the corresponding solutions based on our team’s research achievements were presented, respectively. Last but not least, review conclusions and future works of the data-driven intelligent fault diagnosis technology were proposed to inspire further exploration in this field.

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