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

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
Original languageEnglish
Title of host publicationMaritime Transportation and Harvesting of Sea Resources
Subtitle of host publicationProceedings of the 17th International Congress of the International Maritime Association of the Mediterranean (IMAM 2017)
EditorsC. Guedes Soares, Angelo P. Teixeira
Pages981-989
Number of pages8
Volume2
Publication statusPublished - 9 Jul 2017
Event17th International Congress of the International Maritime Association of the Mediterranean - Lisbon, Portugal
Duration: 9 Oct 201711 Oct 2017
Conference number: 17
http://www.imamhomepage.org/imam2017/index.aspx

Conference

Conference17th International Congress of the International Maritime Association of the Mediterranean
Abbreviated titleIMAM 2017
CountryPortugal
CityLisbon
Period9/10/1711/10/17
Internet address

Keywords

  • ship machinery maintenance
  • system modelling
  • engine performance

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