A multinomial process tree for reliability assessment of machinery in autonomous ships

Abstract

Maritime Autonomous Surface Ships have received a significant amount of attention in recent projects. They promise a reduction in marine accidents and mitigation of human errors. Most of the ongoing research effort is directed toward autonomous navigation and cybersecurity. However, the importance of a machinery plant in the engine room that can operate reliably without human attendance is hardly investigated. To prevent failures in such systems and extend the interval between required human interventions, it is essential to improve their reliability. This paper aims to present a systematic approach to evaluate the reliability of an autonomous system under the influence of uncertain disruptions and to predict failure rates of unattended machinery plants. A Multinomial Process Tree is used to model failures in the main failure-sensitive components. Hierarchical Bayesian Inference is adopted to facilitate the prediction of frequencies of disruptive events and estimate the entire system's failure rate. The outcome of this research enables design strategies to improve the reliability of autonomous ships and prevent Fatal Technical Failure during the operation. This allows assessing whether a given machinery plant is sufficiently reliable to be used on unmanned ships. A case study is considered to demonstrate the application of the presented method.