基于多保真度数字孪生和FMEA知识增强的通用航空飞机智能故障诊断方法
An Intelligent Fault Diagnosis Method for General Aviation Aircraft Based on Multi-Fidelity Digital Twin and FMEA Knowledge Enhancement
AI总结：
本文提出基于多保真度数字孪生的智能故障诊断框架，通过高保真飞行动态仿真、FMEA驱动故障注入、多保真残差特征提取和大语言模型增强的可解释报告生成模块，解决通用航空飞机故障诊断中的数据稀缺、故障类型多样和故障特征弱等问题。
摘要：
通用航空飞机的故障诊断面临数据稀缺、故障类型多样和故障特征弱等挑战。本文提出基于多保真度数字孪生的智能故障诊断框架，整合四个模块：高保真飞行动态仿真、FMEA驱动的故障注入、多保真残差特征提取以及大语言模型（LLM）增强的可解释报告生成。通过JSBSim六自由度（6-DoF）飞行动态引擎构建数字孪生，利用半经验传感器合成方程生成23通道发动机健康监测数据。基于故障模式和影响分析（FMEA）模型的三层故障注入引擎模拟19种发动机故障的物理因果传播。提出包含配对镜像残差和GRU替代预测残差的多保真残差计算框架：高保真路径利用名义镜像轨迹获得干净的故障偏差信号，低保真路径通过多步预测GRU替代模型实现在线实时残差计算。1D-CNN分类器对20种故障类别进行端到端诊断。LLM诊断报告引擎结合FMEA知识融合分类结果、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类任务中达到宏F1值96.2%，GRU替代方案在仅0.6%性能损失下实现4.3倍的推理加速。24种方案的比较表明，残差特征质量对诊断性能的贡献比分类器架构大约5倍，确立了“残差质量优先”的设计原则。
英文摘要：
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. 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. 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. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
