Autonomous navigation technologies have been widely adopted in the automotive and aviation sectors, significantly reducing human-error-induced accidents and operational costs. However, their application to maritime systems remains limited due to the complexity of conventional propulsion systems. Electric propulsion ships, with well-defined system boundaries and accessible operational data, offer a promising platform for autonomous navigation. In this study, we propose an FMEA-guided selective multi-fidelity digital twin framework for fault detection, where model fidelity is adaptively selected between low- and high-fidelity models based on risk priority numbers derived from failure mode and effects analysis. This approach enables selective execution of computationally expensive models only under high-risk conditions, thereby improving computational efficiency. In addition, a sliding window-based algebraic aggregation method is employed to achieve lightweight and real-time fault diagnosis. The proposed framework is validated using operational sensor data from a 100 kW electric propulsion ship under multiple fault scenarios, including power supply faults and signal anomalies. Experimental results show that the proposed method reduces computational cost while maintaining stable real-time performance, compared to conventional data-driven AI-based approaches. These results demonstrate that the proposed framework provides an effective and efficient solution for enhancing the reliability and safety of autonomous ship systems.
Shin et al. (Fri,) studied this question.