Health Monitoring for Autonomous Underwater Vehicles Using Fault Tree Analysis

As an autonomous underwater vehicle (AUV) operates over a long period of time in the sea, continuous online monitoring of the vehicle is crucial. By monitoring the health status of the AUV, it is possible not only to avoid a serious damage or even loss of the vehicle, but also to effectively manage the missions being carried out. This paper presents an online health monitoring technique for AUVs using Fault Tree Analysis (FTA). The use of both information about the reliability and performance of subsystems can be highlighted as the main contribution from this work. The whole system is divided into several subsystems for which a fault tree is designed. Then, the system health is evaluated using the given fault tree by considering not only the performance of each component, but also the weighting factors, reliability, and fault status of various parts in each subsystem. In order to determine the health status of the AUV in real-time, the fault tree is structurally analyzed using the information mentioned above. The effectiveness of the proposed method is demonstrated using a set of simulations.