An Intelligent Fault Diagnosis Method for General Aviation Aircraft Based on Multi-Fidelity Digital Twin and FMEA Knowledge Enhancement

基于多保真度数字孪生与FMEA知识增强的通用航空飞机智能故障诊断方法

Abstract: Fault diagnosis of general aviation aircraft faces challenges including scarce real fault data, diverse fault types, and weak fault signatures. This paper proposes an intelligent fault diagnosis framework based on multi-fidelity digital twin, integrating four modules: high-fidelity flight dynamics simulation, FMEA-driven fault injection, multi-fidelity residual feature extraction, and large language model (LLM)-enhanced interpretable report generation.

摘要： 通用航空飞机的故障诊断面临着真实故障数据稀缺、故障类型多样以及故障特征微弱等挑战。本文提出了一种基于多保真度数字孪生的智能故障诊断框架，集成了四个模块：高保真飞行动力学仿真、基于FMEA（故障模式与影响分析）的故障注入、多保真度残差特征提取，以及大语言模型（LLM）增强的可解释性报告生成。

A digital twin is constructed using the JSBSim six-degree-of-freedom (6-DoF) flight dynamics engine, generating 23-channel engine health monitoring data via semi-empirical sensor synthesis equations. A three-layer fault injection engine based on failure mode and effects analysis (FMEA) models the physical causal propagation of 19 engine fault types.

该研究利用JSBSim六自由度（6-DoF）飞行动力学引擎构建数字孪生体，通过半经验传感器合成方程生成23通道的发动机健康监测数据。基于故障模式与影响分析（FMEA）的三层故障注入引擎，对19种发动机故障类型的物理因果传播进行了建模。

A multi-fidelity residual computation framework comprising paired-mirror residuals and GRU surrogate prediction residuals is proposed: the high-fidelity path obtains clean fault deviation signals using nominal mirror trajectories with identical initial conditions, while the low-fidelity path achieves online real-time residual computation through a multi-step prediction GRU surrogate model. A 1D-CNN classifier performs end-to-end diagnosis of 20 fault classes.

本文提出了一种包含“配对镜像残差”和“GRU代理预测残差”的多保真度残差计算框架：高保真路径利用具有相同初始条件的标称镜像轨迹获取纯净的故障偏差信号；低保真路径则通过多步预测GRU代理模型实现在线实时残差计算。此外，使用一维卷积神经网络（1D-CNN）分类器对20类故障进行端到端诊断。

An LLM diagnostic report engine enhanced with FMEA knowledge fuses classification results, residual evidence, and domain causal knowledge to generate interpretable natural language reports. Experiments show the paired-mirror residual scheme achieves a Macro-F1 of 96.2% on the 20-class task, while the GRU surrogate scheme achieves 4.3x inference acceleration at only 0.6% performance cost. Comparison across 24 schemes reveals that residual feature quality contributes approximately 5x more to diagnostic performance than classifier architecture, establishing the “residual quality first” design principle.

由FMEA知识增强的LLM诊断报告引擎，通过融合分类结果、残差证据和领域因果知识，生成可解释的自然语言报告。实验表明，配对镜像残差方案在20类故障任务中达到了96.2%的Macro-F1值，而GRU代理方案在仅损失0.6%性能的情况下实现了4.3倍的推理加速。通过对24种方案的对比分析发现，残差特征质量对诊断性能的贡献度约为分类器架构的5倍，从而确立了“残差质量优先”的设计原则。