HCUNet: Hierarchical Consistency-Based Uncertainty Quantification Network for UAV Fault Diagnosis

Unmanned aerial vehicle (UAV) fault diagnosis is critical for ensuring flight safety, yet existing methods face challenges from class-imbalanced datasets and the lack of reliable uncertainty estimates. We address these issues with HCUNet, a hierarchical consistency-based uncertainty quantification network that integrates multi-level feature aggregation with principled uncertainty estimation. The framework extracts complementary representations through three sequential stages. First, an adaptive data-driven multi-scale convolution module captures shallow multi-frequency patterns. Second, a hierarchical context bridging LSTM encodes middle-level temporal dynamics. Third, a rotary position embedding enhanced attention mechanism extracts deep semantic features. These hierarchical representations are fused by a latent-aware semantic extraction and aggregation module, which employs cross-scale dilated convolutions and random pooling sampling for robust feature integration. A single-pass mechanism disentangles epistemic and aleatoric uncertainties via prediction disagreement and feature divergence, eliminating the need for ensemble models. The hierarchical consistency loss jointly optimizes multi-level classification accuracy, inter-level agreement, and confidence calibration. Experiments on the ALFA dataset demonstrate that HCUNet achieves 99.55% accuracy, 99.12% F1-score, and 0.0024 expected calibration error, outperforming the strongest baseline by 0.66% in accuracy and 64.2% in calibration error reduction.