Fault tree analysis and artificial neural network modelling for establishing a predictive ship machinery maintenance methodology

Y Raptodimos, I Lazakis

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Abstract

A dynamic fault tree model for a ship main engine is developed in order to analyse and identify critical systems/components of the main engine. The identified most critical systems are then used as input in an artificial neural network. An autoregressive dynamic time series neural network modelling approach is examined in a container ship case study, in order to monitor and predict future values of selected physical parameters of the most critical ship machinery equipment obtained from the fault tree analysis. The case study results of the combination of the fault tree analysis and artificial neural network model demonstrated promising prospects for establishing a dense methodology for ship machinery predictive maintenance by successfully identifying critical ship machinery systems and accurately forecasting the performance of machinery parameters.
Original languageEnglish
Title of host publicationInternational Conference on Smart Ship Technology 2017
Place of PublicationLondon
PublisherRoyal Institution of Naval Architects
Number of pages11
Publication statusPublished - 24 Jan 2017
EventSmart Ship Technology 2017 - RINA HQ, 8-9 Northumberland Street, London, WC2N 5DA, London, United Kingdom
Duration: 24 Jan 201725 Jan 2017
https://www.rina.org.uk/Smart_Ships2017.html

Conference

ConferenceSmart Ship Technology 2017
CountryUnited Kingdom
CityLondon
Period24/01/1725/01/17
Internet address

Keywords

  • Fault Tree Analysis
  • neural network
  • maintenance
  • condition monitoring
  • predictive maintenance
  • time series forecasting

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    Raptodimos, Y., & Lazakis, I. (2017). Fault tree analysis and artificial neural network modelling for establishing a predictive ship machinery maintenance methodology. In International Conference on Smart Ship Technology 2017 Royal Institution of Naval Architects.