基于非线性关联挖掘的航空发动机气路故障诊断方法研究-航空发动机智能控制与健康管理专栏

  • 詹轲倚 ,
  • 廖增步 ,
  • 冷文龙 ,
  • 潘一 ,
  • 宋志平
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  • 1. 中国航发贵阳发动机设计研究所
    2. 浙江大学
    3. 西安交通大学

收稿日期: 2026-01-05

  修回日期: 2026-04-27

  网络出版日期: 2026-04-30

基金资助

贵州省重大科技专项项目;中国航发集团自主创新基金

Aero-engine gas path fault diagnosis based on nonlinear correlation mining

  • ZHAN Ke-Yi ,
  • LIAO Zeng-Bu ,
  • LENG Wen-Long ,
  • PAN Yi ,
  • SONG Zhi-Ping
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Received date: 2026-01-05

  Revised date: 2026-04-27

  Online published: 2026-04-30

摘要

气路故障诊断是航空发动机健康管理系统的重要组成部分,其关键在于故障特征提取。近年来,随着深度学习技术的发展,基于图神经网络的气路故障特征提取方法受到广泛关注。然而,传统图神经网络只能提取节点之间的加权求和关系,而忽略了发动机气路系统中普遍存在的非线性耦合关系,导致跨工况诊断准确性和可解释性不足。为此,提出了一种基于非线性关联挖掘的气路故障诊断方法。该方法通过原创的非线性关联挖掘层和改进的图卷积层实现了气路故障特征的可解释提取。通过500架次全寿命期仿真数据对其性能进行了测试验证,所提方法全程未发生虚警,检测率为88.99%,隔离率为98.61%,在收敛性和诊断准确性等方面均显著优于卷积、图卷积、图注意力等对比方法。

本文引用格式

詹轲倚 , 廖增步 , 冷文龙 , 潘一 , 宋志平 . 基于非线性关联挖掘的航空发动机气路故障诊断方法研究-航空发动机智能控制与健康管理专栏[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2026.33321

Abstract

Gas path fault diagnosis is an essential component of aero-engine health management systems, with fault feature extraction being its key aspect. In recent years, with the advancement of deep learning techniques, gas path fault feature extraction methods based on graph neural networks have attracted considerable attention. However, conventional graph neural networks can only capture linear weighted aggregation relationships among nodes while neglecting the nonlinear coupling relationships prevalent in engine gas path systems, resulting in insufficient cross-condition diagnostic accuracy and interpretability. To address this issue, a gas path fault diagnosis method based on nonlinear correlation mining is proposed. This method achieves interpretable extraction of gas path fault features through an original nonlinear correlation mining layer and an improved graph convolutional layer. The performance of the proposed method was validated using full-lifecycle simulation data encompassing 500 flight sorties. The proposed method achieved zero false alarms throughout the entire operational period, with a detection rate of 88.99% and an isolation rate of 98.61%, significantly outperforming comparative methods based on convolutional, graph convolutional, and graph attention networks in terms of convergence and diagnostic accuracy.

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