基于混合驱动的航空发动机气路故障诊断技术综述

doi:10.12305/j.issn.1001-506X.2025.06.16

摘要/Abstract

摘要：

发展基于气路的航空发动机健康管理技术, 对于提高发动机安全和降低维修成本具有重要意义。首先介绍基于气路的航空发动机健康管理技术发展的总体概况。其次, 以模型驱动、数据驱动和混合驱动分类方式, 系统总结气路故障诊断方法的研究现状, 并介绍基于数模混合驱动的故障诊断方法。同时，综述航空发动机建模方法、航空发动机传感器故障诊断方法和航空发动机气路性能预测技术, 并讨论这些方法的特点、优势及不足。最后，总结航空发动机气路故障诊断技术的发展趋势和所面临的挑战。混合驱动方法在提升气路故障诊断精度、泛化性以及工程适用性方面展现出显著潜力，为复杂工况下的健康管理提供了新的发展方向。

关键词: 混合驱动方法, 航空发动机气路故障诊断, 传感器故障诊断, 数据驱动方法

Abstract:

Developing gas path-based health management technology for aero engines is crucial for improving engine safety and minimizing maintenance costs. This paper begins by introducing the evolution of gas path-based health management technology for aero engines. It then systematically summarizes the current research on gas path fault diagnosis methods based on the classification of model-driven, data-driven, and hybrid-driven approaches, and presents the data-model hybrid driven fault diagnosis method. Additionally, the paper reviews aero-engine modeling methods, aero-engine sensor fault diagnosis methods, and aero-engine gas path performance prediction techniques, discussing their characteristics, advantages, and limitations. Finally, the paper concludes by summarizing the emerging trends and challenges of aero-engine gas path fault diagnosis technology. Hybrid-driven methods demonstrate significant potential in enhancing the accuracy, generalization, and engineering applicability of gas path fault diagnosis, providing new directions for health management under complex operating conditions.

Key words: hybrid method, aero engine gas path fault diagnosis, sensor fault diagnostics, data-driven methods

中图分类号:

TP277

陈肖楠, 王奕首, 卿新林. 基于混合驱动的航空发动机气路故障诊断技术综述[J]. 系统工程与电子技术, 2025, 47(6): 1880-1892.

Xiaonan CHEN, Yishou WANG, Xinlin QING. Review of aero engine gas path fault diagnostics technology based on hybrid-driven method[J]. Systems Engineering and Electronics, 2025, 47(6): 1880-1892.

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方法	优势	不足
线性气路分析法	诊断速度快, 适用于小偏差故障	精确的ICM构建难度大; 没有考虑噪声和误差的影响
非线性气路分析法	诊断速度快, 相较于线性气路分析的诊断精度更高	精确的ICM构建难度大; 没有考虑噪声和误差的影响
加权最小二乘法	对含噪声和误差小偏差的诊断效果较好	每个发动机或测试单元都需要一个单独的基线值
卡尔曼滤波法	诊断速度快, 适用于线性问题的故障诊断	非线性问题的诊断精度较差; 卡尔曼滤波器更倾向于将故障发生的原因归结于多个部件
粒子滤波法	粒子滤波技术在非线性、非高斯系统中的应用具有优越性	算法复杂度高, 计算速度较慢

方法	优势	不足
人工神经网络	计算速度快; 泛化能力强	“黑匣子”工作模式, 诊断结果可解释性差
支持向量机	计算速度快; 计算的复杂性取决于支持向量的数目, 而不是样本空间的维数, 避免了“维数灾难”	随着系统非线性程度的增加, 超平面的构建逐渐复杂
专家系统	允许经验和语言形式的知识参与到故障诊断中	专家知识决定诊断精度, 专家知识获取困难; 泛化能力差
模糊逻辑	计算速度快, 模型精度取决于指定规则的精度	泛化能力差