Abstract
Over the next few years, digitalization and automation are expected to be key drivers for maritime transport innovation to be key drivers for maritime transportation innovation. This revolutionary shift in the shipping industry will heavily impact the reliability of the machinery which is intended to be operated remotely with minimum support from humans. Despite a large amount of research into autonomous navigation and control systems in maritime transportation, the evaluation of unattended engine rooms has received very little attention. For autonomous vessels to be effective during their unmanned mission, it is essential for the engine room understand its health condition and self-manage performance. The unattended machinery plant (UMP) should be resilient enough to have the ability to survive and recover from unexpected perturbations, disruptions, and operational degradations. Otherwise, the system may require unplanned maintenance or the operation will stop. Therefore, the UMP must continue its operation without human intervention and safely return the ship to port. This paper aims to develop a machine learning-based model to predict an UMP's performance and estimate how long the engine room can operate without human assistance. A Random Process Tree is used to model failures in the unattended components, while a Hierarchical Bayesian Inference is adopted to facilitate the prediction of unknown parameters in the process. A probabilistic Bayesian Network developed and evaluated the dependent relationship between active and standby components to assess the effect of redundant units in the performance of unattended machinery. The present framework will provide helpful additional information to evaluate the associate uncertainties and predict the untoward events that put the engine room at risk. The results highlight the model's ability to predict the UMP's trusted operation period and evaluate an unattended engine room's resilience. A real case study of a merchant vessel used for short sea shipping in European waters is considered to demonstrate the model's application.
Highlights
The safety of unattended machinery plants in Maritime Autonomous Surface Ships (MASS) is expected to significantly impact maritime trade
This study proposed a probability model for evaluating the perfor mance of unattended machinery plant (UMP) in autonomous shipping and developing a predictive tool for estimating the trusted operation time of the system without human interventions
The model consists of five different steps to predict the randomness of the process and develop a redundancy strategy to in crease the resilience of the engine room under the influence of disrup tions
Summary
The safety of unattended machinery plants in Maritime Autonomous Surface Ships (MASS) is expected to significantly impact maritime trade. Different approaches have recently been investigated for allocating redundancy based on predicting time to failures, such as assuming exponential distribution and Erlang distri butions [30,31] The latter assumption can model the time-varying hazard rate; it cannot consider the process’s uncertainty, especially if the variability of data is concerned. This paper aims to develop a probabilistic approach for modeling redundancy of the UMP as an approach to estimate the system’s resil ience to untoward changes To this end, firstly a stochastic model is proposed to overcome the hurdles in predicting time-to-failures for assessing the performance of UMPs. A Random Process Tree (RPT) is developed to address the operation’s uncertainty and categorize the system’s non-nominal conditions into critical and non-critical failures.
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