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. Attached Files Name Description MIMEType Size Downloads 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: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 29 May 2017, 11:18:50 EST Wed, 31 May 2017, 09:18:54 EST Thu, 01 Jun 2017, 05:00:06 EST Thu, 01 Jun 2017, 08:24:08 EST Mon, 05 Jun 2017, 16:20:35 EST Tue, 06 Jun 2017, 06:06:20 EST Tue, 06 Jun 2017, 16:09:17 EST Tue, 06 Jun 2017, 16:12:40 EST Tue, 06 Jun 2017, 16:13:44 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 612 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Mon, 05 Jun 2017, 16:20:35 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.