An artificial neural network approach for predicting the performance of ship machinery equipment

Yiannis Raptodimos, Iraklis Lazakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Inadequate ship machinery maintenance can increase equipment failure posing a threat to the environment, affecting ship performance, having a great impact in terms of business losses by reducing ship availability and increasing downtime and moreover increasing the potential of major accidents occurring, endangering lives onboard. Efforts have being made to transform corrective/preventive maintenance techniques into predictive ones. Condition monitoring is considered as a major part of predictive maintenance. It assesses the operational health of equipment, in order to provide early warning of potential failure such that preventive maintenance action may be taken. Condition monitoring is defined as the collection and interpretation of the relevant equipment parameters for the purpose of the identification of the state of equipment changes from normal conditions and trends of the health of the equipment. The equipment condition and the fault developing trend are often highly nonlinear and time-series based. Artificial Neural Networks (ANNs) can be used due to their potential ability in nonlinear time-series trend prediction. Therefore this paper proposes the use of an autoregressive dynamic time series ANN in order to monitor and predict selected physical parameters of ship machinery equipment that contribute to the overall performance and availability, in order to predict their future values that will illustrate their performance state that will eventually lead to the correct maintenance actions and decisions.
Original languageEnglish
Title of host publicationMaritime Safety and Operations 2016 Conference Proceedings
Number of pages7
Publication statusPublished - 13 Oct 2016
EventInternational Conference of Maritime Safety and Operations 2016 - University of Strathclyde, Glasgow, United Kingdom
Duration: 13 Oct 201614 Oct 2016


ConferenceInternational Conference of Maritime Safety and Operations 2016
Abbreviated titleMSO 2016
Country/TerritoryUnited Kingdom
Internet address


  • predictive maintenance
  • artificial neural network (ANN)
  • time series analysis
  • condition monitoring
  • ship machinery maintenance
  • ship performance


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