Implementation of a self-learning algorithm for main engine condition monitoring

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos; Soares, C. Guedes and Teixeira, Angelo P., eds. (2017) Implementation of a self-learning algorithm for main engine condition monitoring. In: Maritime Transportation and Harvesting of Sea Resources. CRC Press, PRT, pp. 981-989. ISBN 978-0-8153-7993-5

Abstract

Ship machinery maintenance can be seen as a convoluted optimisation problem where managerial, economic, and technical aspects are considered for vessel operation to remain sustainable and profitable. Recent literature shows that condition monitoring of ship systems has been tackled on single and independent component level. However, there has recently been a research tendency towards holistic system modelling. In this respect, this paper presents a methodology for intelligent, system-level modelling for the monitoring of main engine performance utilising data acquired through noon-reports. The proposed methodology will train a main engine expected-performance model. Training is based on one-class Support Vector Machine (SVM) classifier. Newly-acquired data are compared against model output and the probability of belonging to the same performance profile as used for model training is estimated. This will eventually lead to increasing ship operability and income through operational enhancement and minimisation of ship downtime. Two case studies are included, one utilising main engine data and one diesel generator data. Both are complemented by a sensitivity analysis, showing successful results in the recognition of deviant, abnormal ship machinery conditions.

ORCID iDs

Gkerekos, Christos ORCID: https://orcid.org/0000-0002-3278-9806

Lazakis, Iraklis ORCID: https://orcid.org/0000-0002-6130-9410

Theotokatos, Gerasimos ORCID: https://orcid.org/0000-0003-3547-8867

• Share and Export
  

  RDF+XMLBibTeXRIOXX2 XMLRDF+N-TriplesJSONDublin CoreAtomSimple MetadataReferMETSHTML CitationASCII CitationOpenURL ContextObjectEndNoteOpenURL ContextObject in SpanMODSMPEG-21 DIDLEP3 XMLData Cite XMLRIS (EndNote Online, Zotero, Ref manager, etc)RDF+N3Multiline CSV
• Item metadata

  Item type:	Book Section
  ID code:	62315
  Dates:	Date Event 9 July 2017 Published
  Subjects:	Naval Science > Naval architecture. Shipbuilding. Marine engineering
  Department:	Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering
  Depositing user:	Pure Administrator
  Date deposited:	09 Nov 2017 16:12
  Last modified:	11 Nov 2024 15:11
  Related URLs:	Publisher
  URI:	https://strathprints.strath.ac.uk/id/eprint/62315