A prediction tool for maintenance costs estimation during the design process of a ship engine room

Abstract When dealing with maintenance in ships engine room, the space available around machinery and systems (clearance) plays an important role and may significantly affect the cost of the maintenance intervention. In a first part of a current research study Gualeni et al. (Ship Technol Res, 10.1080/09377255.2021.2020949, 2022), a quantitative relation between the maintenance costs increment due to the clearance reduction is determined, using a Bayesian approach to General Linear Model (GLM), with reference to a single item/component of a larger system Sánchez-Herguedas et al. (Reliability Eng Syst Saf 207: 107394, 2021). This paper represents the second part of the activity and it enforces a systemic view over the whole machinery or system Sanders and Klein (Proc Comput Sci 8:413–419, 2012). The aim is to identify not only the relation between maintenance costs and clearance reduction, but also how the clearance reductions of the single components/items interact and affect the whole system/machinery accessibility and maintainability, meant as relevant emerging properties. The system emerging properties are investigated through the design and application of a Hidden Markov Model Salvatier et al. (Peer J Comput Sci 2: e55, 2016); i.e., the system is modeled by a Markov process with unobservable states. The sequence of states is the maintainability of the system (which incorporates each one of the single components) while the evidence is the increase in cost of maintenance related to the space reduction. By predicting a sequence of states, it is therefore possible to predict the interactions between the system components clearances and determine how the emerging maintainability property is affected by the engine room design.