Condition monitoring of engine lubrication oil of military vehicles: a machine learning approach

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Condition monitoring of engine lubrication oil of military vehicles: a machine learning approach

Mohamed, Shady, Le, Vu, Lim, Chee Peng, Nahavandi, Saeid, Yen, Leong, Gallasch, Guy Edward, Baker, Stephen, Ludovici, David, Draper, Nick and Wickramanayake, Vish 2017, Condition monitoring of engine lubrication oil of military vehicles: a machine learning approach, in AIAC 2017 : Proceedings of the 17th Australian International Aerospace Congress, Australian Defence Science and Technology Group (DST Group), [Melbourne, Vic.], pp. 1-7.

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Title	Condition monitoring of engine lubrication oil of military vehicles: a machine learning approach
Author(s)	Mohamed, Shady orcid.org/0000-0002-8851-1635 Le, Vu orcid.org/0000-0003-0648-6112 Lim, Chee Peng orcid.org/0000-0003-4191-9083 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Yen, Leong Gallasch, Guy Edward Baker, Stephen Ludovici, David Draper, Nick Wickramanayake, Vish
Conference name	Aerospace Congress (17th : 2017 : Melbourne, Victoria)
Conference location	Melbourne, Victoria
Conference dates	26-28 Feb. 2017
Title of proceedings	AIAC 2017 : Proceedings of the 17th Australian International Aerospace Congress
Publication date	2017
Start page	1
End page	7
Total pages	7
Publisher	Australian Defence Science and Technology Group (DST Group)
Place of publication	[Melbourne, Vic.]
Keyword(s)	Lubrication oil classification machine learning health and usage monitoring system VHUMS military vehicle
Summary	Lubrication oil plays an important role in maintaining the health and performance of a land vehicle engine. Accurate condition monitoring of lubrication oil enables an effective predictive maintenance regime to be established. This can extend engine life as well as reduce over or under-servicing and other unnecessary maintenance costs. Machine learning models are useful for mining meaningful patterns from data samples. In this research, through the application of such models, we classify the condition of engine lubrication oil based on data from the Vehicle Health and Usage Monitoring System and laboratory test results of lubrication oil from a cohort of military land vehicles. The oil condition is classified into three categories: normal, degraded, and unsuitable. Feature selection methods are used to identify the best feature set for representing the lubrication oil condition. Importantly, the machine learning models employed provide the predicted output with justification in the form of explanatory rules pertaining to the lubrication oil condition. The findings indicate that (i) a good feature selection method is necessary to reduce the dimensionality of the feature set used for classification; (ii) machine learning provides a viable method for classifying oil condition with understandable justifications.
Language	eng
Field of Research	099999 Engineering not elsewhere classified
Socio Economic Objective	970110 Expanding Knowledge in Technology
HERDC Research category	E1 Full written paper - refereed
Persistent URL	http://hdl.handle.net/10536/DRO/DU:30096947

Document type:	Conference Paper
Collections:	Centre for Intelligent Systems Research GTP Research


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