Maritime shipping, with a significant role in global trade, confronts various accidents leading to loss of lives, properties, and the environment. Shipping 4.0 technologies are scaling up to address this problem by employing real-time data-driven technologies, including cyber-physical systems, advanced tracking and tracing, intelligent systems, and big data analytics. Despite growing attention, there is a general lack of clarity on the level and direction of progress in this field. Accordingly, this study aims to identify critical shipping accident risks, analyze the role of relevant shipping 4.0 technologies in controlling these risks, and consolidate the findings into a conceptual guiding framework directing future developments. Accordingly, a systematic review is performed that reveals how shipping 4.0 approaches address critical accident risks and the gaps that still exist. Overall, we found that the collision is the most frequent accident referred to, while the most frequent technology to control the accidents is the Automatic Identification System. In contrast, we see an evident lack of cloud computing, internet-of-things, and big data analytics, which play crucial roles in current industry 4.0 developments.@en

Analyzing the reliability of autonomous ships has recently attracted attention mainly due to epistemic uncertainty (lack of knowledge) integrated with automatic operations in the maritime sector. The advent of new random failures with unrecognized failure patterns in autonomous ship operations requires a comprehensive reliability assessment specifically aiming at estimating the time in which the ship can be trusted to be left unattended. While the reliability concept is touched upon well through the literature, the operational trustworthiness needs more elaboration to be established for system safety, especially within the maritime sector. Accordingly, in this paper, a probabilistic approach has been established to estimate the trusted operational time of the ship machinery system through different autonomy degrees. The uncertainty associated with ship operation has been quantified using Markov Chain Monte-Carlo simulation from likelihood function in Bayesian inference. To verify the developed framework, a practical example of a machinery plant used in typical short sea merchant ships is taken into account. This study can be exploited by asset managers to estimate the time in which the ship can be left unattended. Keywords: reliability estimation, Bayesian inference, autonomous ship, uncertainty.

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