Application of artificial neural network and dynamic fault tree analysis to enhance reliability in predictive ship machinery health condition monitoring

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Abstract

The electric power generation system of most ships is powered by a group of diesel generators generally with redundancy to accommodate peak load periods or critical situations. Blackouts onboard ships portents a potential danger to navigation as well as the security and safety of the ship. Thus, understanding the factors affecting the reliability of individual diesel generators and the most critical component to failure is key to ensuring reliable performance of the generators. Therefore, this study was conducted on diesel power generation plant consisting of four Marine Diesel Generators onboard an Offshore Patrol Vessel (OPV). Findings indicates relatively low reliability, of less than 60 per cent within the first 24 months of the 78 operational months data analysed. Similarly, reliability importance measures were adopted to identify Critical components which contribute at least 40 per cent of failures on the sub systems of the diesel generators. The use of dynamic spare gates in the dynamic fault tree analysis has highlighted possible improvements through maintenance action or use of sensors to improve sub-system as well as individual diesel generator’s reliability. Additionally, Artificial Neural Networks classification using unsupervised learning was conducted to identify patterns in the data that signifies the onset of performance degradation in the diesel generators.
Original languageEnglish
Number of pages13
Publication statusPublished - 18 Sep 2021
Event2nd International Conference on Ship and Marine Technology - Istanbul, Turkey
Duration: 16 Sep 202117 Sep 2021
https://www.gmoshipmar.org/GMOSHIPMAR2021/

Conference

Conference2nd International Conference on Ship and Marine Technology
Abbreviated titleGMO-SHIPMAR 2021
Country/TerritoryTurkey
CityIstanbul
Period16/09/2117/09/21
Internet address

Keywords

  • marine diesel generator
  • dynamic fault trees
  • reliability importance measures
  • critical components

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