One class classification based anomaly detection for marine engines

Edward Smart, Neil Grice, Hongjie Ma, David Garrity, David Brown

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

Although extensive research has been undertaken to detect faults in marine diesel engines, significant challenges still remain such as the need for non-invasive monitoring methods and the need to obtain rare and expensive datasets of multiple faults from which machine learning algorithms can be trained upon. This paper presents a method that uses non-invasive engine monitoring methods (vibration sensors) and doesn’t require training on faulty data. Significantly, the one class classification algorithms used were tested on a very large number (12) of actual diesel engine faults chosen by diesel engine experts and maritime engineers, which is rare in this field. The results show that by learning on only easily obtainable healthy data samples, all of these faults, including big end bearing wear and ’top end’ cylinder leakage, can be detected with very minimal false positives (best balanced error rate of 0.15%) regardless of engine load. These results were achieved on a test engine and the method was also applied to an operational vehicle/passenger ferry engine where it was able to detect a fault on one of the cylinders that was confirmed by the vessel’s engineering staff. Additionally, it was also able to confirm that a sensor fault occurred. Significantly it highlights how the ’healthiness’ of an engine can be assessed and monitored over time, whereby any changes in this health score can be noted and appropriate action taken during scheduled maintenance periods before a serious fault develops.
Original languageEnglish
Title of host publicationIntelligent Systems: Theory, Research and Innovation in Applications
EditorsRicardo Jardim-Goncalves, Vassil Sgurev, Vladimir Jotsov, Janusz Kacprzyk
Place of PublicationSwitzerland
PublisherSpringer International Publishing
Chapter10
Pages223-245
Volume864
ISBN (Electronic)978-3-030-38704-4
ISBN (Print)978-3-030-38703-7
DOIs
Publication statusPublished - 4 Mar 2020

Publication series

NameStudies in Computational Intelligence
PublisherSpringer
Volume864
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Keywords

  • condition monitoring
  • fault diagnosis
  • support vector machines

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