An interpretable weakly-supervised contrastive learning framework for UAV fault diagnosis and component-level localization with partial annotations

Unmanned aerial vehicles (UAVs) are critical in modern industrial applications, yet their reliability is challenged by complex fault modes and limited labeled data for diagnosis. This paper proposes an interpretable weakly-supervised contrastive learning framework that simultaneously addresses accurate fault classification and precise component-level localization under partial annotations. Our method integrates multi-scale feature extraction using parallel one-dimensional convolutions with channel and temporal attention mechanisms to capture discriminative patterns while highlighting critical sensor channels and fault-relevant time steps. The framework incorporates contrastive learning with Gaussian noise injection and time warping augmentations to leverage unlabeled data, combined with a dynamic pseudo-label update mechanism that iteratively refines predictions. A specialized fine-grained localization module with fault-type-specific branches enables precise mapping of faults to specific components. Experimental results on the real UAV dataset demonstrate superior performance, achieving a macro F1-score of 0.89 with only 10% labeled data, outperforming state-of-the-art baselines. The framework also achieves near-perfect component-level localization under weak supervision. This work establishes a new paradigm for trustworthy industrial AI by integrating weak supervision efficiency with interpretable diagnostics, offering both high accuracy and operational transparency for UAV maintenance systems.