A hybrid human reliability analysis approach for a remotely-controlled maritime autonomous surface ship (MASS- degree 3) operation

Abstract

Maritime autonomous surface ships (MASS) are one of the hot topics in maritime transportation even though they bring many challenges in terms of safety, security, and environment. This paper tackles the safety-related challenges of remotely controlled ships without seafarers on board but controlled at the shore. In this context, the reliability of the operator is of paramount importance for safe and efficient MASS operation. This paper performs systematic human reliability analysis for the operator of MASS (for degree 3) under the Bayesian belief network (BBN) and evidential reasoning (ER)- cognitive reliability and error analysis method (CREAM) approach. In the model, BBN is capable of determining the probability distribution of Contextual Control Modes in CREAM, while ER tackles the uncertainty and subjectivity of expert judgments. The outcome of the paper shows that the human reliability for remote control MASS operation is 8.88E-01. Besides its robust theoretical background, the paper will provide the utmost contributions to operators, managers, safety inspectors, and ship owners of MASS for safer and reliable operations.