Inadequate ship machinery maintenance can increase equipment failure posing a threat to the environment, affecting performance, having a great impact in terms of business losses by reducing ship availability, increasing downtime and moreover increasing the potential of major accidents occurring and endangering lives on-board. This paper aims to provide a systematic approach for identifying critical ship machinery systems/components and to analyse their physical parameters. Critical ship main engine systems/components are used as input in a dynamic time series neural network, in order to monitor and predict future values of physical parameters related to ship critical systems. The critical main engine systems/components and their relevant parameters to be monitored are identified though the combination of Fault Tree Analysis (FTA) and Failure Mode and Effects Analysis (FMEA). A case study of a Panamax size container ship is presented in which Artificial Neural Networks (ANN) are used to predict the upcoming values of all main engine cylinders exhaust gas temperatures. The forecasted results were validated through comparison with actual observations recorded on board the ship. The proposed hybrid methodology successfully presents a systematic approach for initially identifying critical systems/components through reliability modelling and tools and subsequently monitoring their physical parameters through neural networks. 2. Research Background/Literature The evolution of maintenance was based not solely on technical but rather on techno-economic considerations according to Pintelon and Parodi-Herz (2008). Furthermore, according to Arunraj and Maiti (2007), maintenance policies can be categorised into four generations as seen in Figure 1. The fourth generation is the most recent one, which focuses on condition based maintenance, condition monitoring and failure eliminations. It concentrates on reducing the proportion of equipment failures and overall levels of failure probability through various tools and strategies, based on preventive and predictive maintenance approaches.

In this paper we present an innovative toolkit that Danaos Corporation developed and deployed to optimize ship routing. Operations Research In Ship MAnagement (ORISMA) provides a clear answer to the conventional dilemma of least-cost voyage versus faster voyage. ORISMA maximizes revenue by using relevant information, including financial data, hydrodynamic models, weather conditions, and marketing forecasts. It considers the financial benefits after ship voyage completion to optimize the fleetwide performance instead of single-vessel performance. Using operations research and expert knowledge, we developed ORISMA to include world-class capabilities in scheduling optimization, intelligent voyage planning, ship bunkering, and chartering. In addition to maximizing Danaos’ profit, it helps the company to minimize carbon emissions, reduce staff workload, and increase customer satisfaction.

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