Optical analysis for condition based monitoring of oxidation degradation in lubricant oil

This paper presents the optical analysis of transmittance variation in lubricant oil due to the oxidation by using Embedded MATLAB Function (EMF) tools. Recently, the increasing amount of used engine oil is due to the car's manufacturer that recommended users to change their engine oil at a con...

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书目详细资料
发表在:ICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings
主要作者: Idros M.F.M.; Ali S.; Islam M.S.
格式: Conference paper
语言:English
出版: 2012
在线阅读:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867964427&doi=10.1109%2fICIAS.2012.6306110&partnerID=40&md5=aa7cb8d91f7e83af77815e18010295c5
实物特征
总结:This paper presents the optical analysis of transmittance variation in lubricant oil due to the oxidation by using Embedded MATLAB Function (EMF) tools. Recently, the increasing amount of used engine oil is due to the car's manufacturer that recommended users to change their engine oil at a constant time or according to mileage interval. This will make a possibility of substantial increases of used engine oil because it changed more frequently than necessary. Therefore, a condition based technique is introduced to monitor the oxidation in lubricant engine oil by using EMF. Band location of 1050-1250cm 1 and 1700-1730cm 1 were identified as the most amount of absorbed infrared radiation by carboxylic acid and hydrocarbon as the degradation was increased. The degradation has been limited to the percentage reduction from the new oil by three different categories as a different kind of driving condition. By applying a regression analysis in EMF, the result shows that the slopes of degradation at a continuous stage of degradation were influencing the prediction of degradation limit. The written algorithm in EMF is potentially developed in Register Transfer Level (RTL) for further application. © 2011 IEEE.
ISSN:
DOI:10.1109/ICIAS.2012.6306110