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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, ShadyORCID iD for Mohamed, Shady orcid.org/0000-0002-8851-1635
Le, Vu
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Nahavandi, Saeid
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
Collection: Centre for Intelligent Systems Research
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Created: Mon, 05 Jun 2017, 16:20:35 EST

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