Predictive maintenance decision support system for enhanced energy efficiency of ship machinery

Michala, A. L. and Lazakis, I. and Theotokatos, G. (2015) Predictive maintenance decision support system for enhanced energy efficiency of ship machinery. In: International Conference on Shipping in Changing Climates, 2015-11-24 - 2015-11-26, Technology & Innovation Centre. (

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A decision support system (DSS) is an application that analyses data and presents results to users. DSS rapidly shift through huge amount of available data and thus allowing for faster analysis of condition monitoring data early detection of faults and improved allocation of resources. DSS can also predict and plan for future ship operators’ needs in order to optimize ship machinery operations. Such a system can provide substantial benefits to the maritime industry in terms of energy efficiency as the operation of the vessel can be optimised towards this end. As part of the INCASS (Inspection Capabilities for Enhanced Ship Safety) EU FP7 project, this paper presents a novel DSS solution which interrogates data from dynamic condition monitoring and compares them with historic data to present decision support information onboard a ship. To provide for Condition Based inspection and criticality based maintenance for ship machinery, data is acquired and stored for analysis through the DSS. Moreover surveys involving off-line and real time on-line measurement approaches are combined to provide a more complete monitoring method. The result is a reliable user friendly graphical interface (GUI) developed in Java language that can be employed onboard any vessel and can provide relevant and on-time information. The proposed actions from the DSS target energy efficient operation and reduction of fuel consumption and ship emissions. Moreover, a major factor taken into account through the prediction mechanism of the DSS is to assist in better spare parts scheduling and prioritizing ship inspection, maintenance and repairs towards enhanced and efficient ship operations.


Michala, A. L. ORCID logoORCID:, Lazakis, I. ORCID logoORCID: and Theotokatos, G. ORCID logoORCID:;