分层架构解决飞行器健康监测中的精度与算力矛盾。
A Heterogeneous Long-Micro Scale Cascading Architecture for General Aviation Health Management
- 分离全局异常检测与局部故障分类，破解感受野悖论。
- 在28,935架次数据上提升4-8%安全指标，训练提速4.2倍。
- 适合资源受限的航空设备实时健康监控场景。
通用航空机队扩张对智能健康监测提出计算约束下的新要求。真实飞行健康诊断需在极端类别不平衡和环境不确定性下平衡精度与算力。现有端到端方法面临感受野悖论：全局注意力引入过多操作异质性噪声，影响细粒度故障分类；而局部约束又牺牲了异常检测所需的跨时序上下文。本文提出一种面向通用航空健康管理的异构级联架构，即长-微尺度诊断仪（LMSD），显式解耦全序列注意力的全局异常检测与受限感受野的微尺度故障分类，有效缓解感受野悖论并降低训练开销。基于知识蒸馏的可解释模块提供物理可追溯的解释，支持安全关键验证。在公开的国家通用航空飞行信息数据库（NGAFID，含28,935架次、36个类别）上的实验表明，相比端到端基线，该方法在安全关键指标（MCWPM）上提升4%-8%，训练加速4.2倍，模型压缩46%。结论表明，该人工智能驱动的异构架构为航空设备健康管理提供了可部署方案，具备未来与数字孪生集成潜力，在资源受限环境下仍满足严格安全要求。
原文摘要 · Abstract (English)
BACKGROUND: General aviation fleet expansion demands intelligent health monitoring under computational constraints. Real-world aircraft health diagnosis requires balancing accuracy with computational constraints under extreme class imbalance and environmental uncertainty. Existing end-to-end approaches suffer from the receptive field paradox: global attention introduces excessive operational heterogeneity noise for fine-grained fault classification, while localized constraints sacrifice critical cross-temporal context essential for anomaly detection. METHODS: This paper presents an AI-driven heterogeneous cascading architecture for general aviation health management. The proposed Long-Micro Scale Diagnostician (LMSD) explicitly decouples global anomaly detection (full-sequence attention) from micro-scale fault classification (restricted receptive fields), resolving the receptive field paradox while minimizing training overhead. A knowledge distillation-based interpretability module provides physically traceable explanations for safety-critical validation. RESULTS: Experiments on the public National General Aviation Flight Information Database (NGAFID) dataset (28,935 flights, 36 categories) demonstrate 4--8% improvement in safety-critical metrics (MCWPM) with 4.2 times training acceleration and 46% model compression compared to end-to-end baselines. CONCLUSIONS: The AI-driven heterogeneous architecture offers deployable solutions for aviation equipment health management, with potential for digital twin integration in future work. The proposed framework substantiates deployability in resource-constrained aviation environments while maintaining stringent safety requirements.
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