An MBSE Driven Framework for Automated Generation of RAM Risk Models from Maritime Vessel System Architectures
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Abstract
Reliability, availability, and maintainability (RAM) analysis in maritime systems depends on risk models that are typically developed manually from design documentation. This process is time-consuming, error-prone, and weakly connected to system architecture models. This work presents an MBSE-driven framework that transforms SysML system models into RAAML-based risk models, establishing a direct link between system design and RAM analysis.
The framework defines a multi-layer mapping approach covering concept, semantic, and meta-model levels. Structural, behavioural, and interface elements in SysML are systematically converted into risk modelling constructs, enabling the automatic generation of reliability block diagrams, mission profiles, and failure propagation models. Quantitative parameters, including failure rates and maintenance data, are incorporated to support system-level reliability evaluation. The derived propagation paths and critical dependencies also provide a basis for sensor placement and condition monitoring design, supporting PHM applications.
A maritime case study based on a remotely operated vehicle (ROV) demonstrates the applicability of the approach. The results show a reduction in RAM modelling effort of 34% to 55% compared to conventional methods, with greater benefits observed for more complex systems. The framework improves modelling consistency, traceability, and scalability by deriving risk models directly from system architecture.
How to Cite
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Model-Based System Engineering(MBSE), Prognostics and Health Management (PHM), Reliability Availability and Maintainability (RAM), Risk Modelling, Maritime Vessel, Model Transformation, System Architecture, SysML, RAAML, Digital Engineering
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