MELF-XFD: Trustworthy and Interpretable Multi-Expert Fusion for Safety-Critical Fault Diagnosis in Autonomous Underwater Vehicles

Autonomous underwater vehicles (AUVs) operating in harsh, communication-limited underwater environments require robust fault diagnosis systems to ensure mission success and safety. Existing model-based approaches suffer from hydrodynamic modeling inaccuracies, while data-driven methods typically employ monolithic architectures that lack interpretability and real-time deployment awareness. This paper proposesMulti-ExpertLightweightFusion Model with Ethical &ExplainableFaultDiagnosis (MELF-XFD), a novel model-free framework that addresses these limitations through physically-grounded multi-domain signal decomposition. MELF-XFD performs explainable fault diagnosis by fusing temporal, spectral, and statistical representations of multivariate AUV time-series data using lightweight expert networks with adaptive weighting for real-time onboard deployment. Experimental results on a public AUV benchmark with five fault classes show that MELF-XFD outperforms existing methods, achieving a 95.7% macro-F1 score, 90.0% severe fault recall, and 39 ms inference latency. It attains the highest composite diagnostic criterion around 93.6%, balancing accuracy, safety-critical sensitivity, and computational efficiency. A low expected calibration error of 0.0880 ensures reliable confidence estimates for safety-critical deployment. Ablation studies confirm the critical role of temporal and frequency experts, while adaptive fusion enables interpretable fault attribution, establishing MELF-XFD as a practical and deployment-ready solution.