Risk Analysis of Autonomous Underwater Vehicle Operation in a Polar Environment Based on Fuzzy Fault Tree Analysis

Abstract

Autonomous underwater vehicles have long been used in marine explorations, and their application in recent polar expeditions is particularly noteworthy. However, the complexity and extreme conditions of the polar environment pose risks to the stable operation of autonomous underwater vehicles. This study adopted the methodology of fuzzy fault tree analysis to deeply analyze the operational risks of autonomous underwater vehicles in polar environments. While traditional fault tree analysis maps the causal relationships and probabilities between basic and intermediate events, fuzzy fault tree analysis models the uncertainty of data and determines the failure probability by integrating expert opinions. This study revealed that polar environment-induced failures play a more substantial role in autonomous underwater vehicle loss in polar regions than inherent system failures. The study identified ‘recovery failure’ and ‘poor communication’ as the major risk factors facing autonomous underwater vehicles in polar environments, exhibiting the highest failure probabilities. Specifically, among various polar environmental factors, ‘large ice concentration’, ‘ice thickness’, and ‘roughness of ice underside’ under ‘bad’ conditions were found to have a significant impact on the autonomous underwater vehicle’s failure probability. The fuzzy fault tree analysis method in this study successfully filled the gap created by the absence of historical data by effectively incorporating expert opinions, enabling a quantitative presentation of the impact of polar environments, which has been previously difficult to convey in qualitative terms.

Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis

“…An AUV as a whole can be referred to as a multi-state system if each subsystem is thought of as a single state. Based on the status of each subsystem, which has a certain performance level and probability, the expected instantaneous performance (EIP) can be computed as follows in Equation (2).…”

“…As a result, when carrying out a mission, it is essential to monitor the performance and dependability of AUVs. Numerous studies, including those on AUV risk analysis [2], uncertainty estimation [3], and design techniques [4], are being conducted to study dependability. For evaluating the performability of AUVs, it is necessary to focus on the mission-dependent components and/or subsystems, because each mission exploits different combinations of devices and equipment.…”