Undersea terrain and resource exploration missions using autonomous underwater vehicles (AUVs) require a great deal of time. Therefore, it is necessary to monitor the state of the AUV in real time during the mission. In this paper, we propose an online health-monitoring method for AUVs using fault-tree analysis. The entire system is divided into four subsystems. Fault trees of each subsystem are designed based on the information of performance and reliability. Using the given subsystem fault trees, the health status of the entire system is evaluated by considering the performance, reliability, fault status, and weight factors of the parts. The effectiveness of the proposed method is demonstrated through simulations with various scenarios.

This paper presents a phased fault tree analysis (phased-FTA)-based approach to evaluate the performability of Autonomous Underwater Vehicles (AUVs) in real time. AUVs carry out a wide range of missions, including surveying the marine environment, searching for specific targets, and topographic mapping. For evaluating the performability of an AUV, it is necessary to focus on the mission-dependent components and/or subsystems, because each mission exploits different combinations of devices and equipment. In this paper, we define a performability index that quantifies the ability of an AUV to perform the desired mission. The novelty of this work is that the performability of the AUV is evaluated based on the reliability and performance of the relevant resources for each mission. In this work, the component weight, expressing the degree of relevance to the mission, is determined using a ranking system. The proposed ranking system assesses the performance of the components required for each mission. The proposed method is demonstrated under various mission scenarios with different sets of faults and performance degradations.