A Heterogeneous Long-Micro Scale Cascading Architecture for General Aviation Health Management

TL;DR

This paper introduces a heterogeneous cascading AI architecture for general aviation health management that improves fault detection accuracy and efficiency while providing interpretable safety explanations.

Contribution

It proposes a novel Long-Micro Scale Diagnostician architecture that decouples anomaly detection from fault classification, enhancing performance and interpretability.

Findings

Achieved 4-8% improvement in safety metrics on NGAFID dataset.

Reduced training time by 4.2 times and model size by 46%.

Demonstrated deployability in resource-constrained aviation environments.

Abstract

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.