When dealing with maintenance in the ship's engine room, the space available around machinery and systems plays an important role. A proper clearance is usually indicated by the system supplier to perform maintenance operations. This space depends on the items dimensions, the kind of intervention and on the human operator, to avoid uncomfortable or dangerous positions. However sometimes the limited space in the engine rooms (as in warships, passenger ships, research vessels) implies critical issues in complying with such ideal clearances. This work aims to develop a tool to define a relation between the maintenance costs increase and the clearance reduction, regarding a single item and/or for the whole system. This tool improves the decision-making process during the design of engine room’s layout, enabling the comparison among different solutions in terms of operational costs. The approach relies on data-driven models and Bayesian inference. The predictive tool, inserted on the Systems Engineering methodology, has been tested on a real case.

Accessibility for maintenance in the engine room: development and application of a prediction tool for operational costs estimation

Gualeni P.;Vairo T.
2022

Abstract

When dealing with maintenance in the ship's engine room, the space available around machinery and systems plays an important role. A proper clearance is usually indicated by the system supplier to perform maintenance operations. This space depends on the items dimensions, the kind of intervention and on the human operator, to avoid uncomfortable or dangerous positions. However sometimes the limited space in the engine rooms (as in warships, passenger ships, research vessels) implies critical issues in complying with such ideal clearances. This work aims to develop a tool to define a relation between the maintenance costs increase and the clearance reduction, regarding a single item and/or for the whole system. This tool improves the decision-making process during the design of engine room’s layout, enabling the comparison among different solutions in terms of operational costs. The approach relies on data-driven models and Bayesian inference. The predictive tool, inserted on the Systems Engineering methodology, has been tested on a real case.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1078746
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