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Image Analysis in the Field of Oil Contamination Monitoring
Linköping University, Department of Electrical Engineering, Computer Vision.
2011 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Monitoring wear particles in lubricating oils allows specialists to evaluate thehealth and functionality of a mechanical system. The main analysis techniquesavailable today are manual particle analysis and automatic optical analysis. Man-ual particle analysis is effective and reliable since the analyst continuously seeswhat is being counted . The drawback is that the technique is quite time demand-ing and dependent of the skills of the analyst. Automatic optical particle countingconstitutes of a closed system not allowing for the objects counted to be observedin real-time. This has resulted in a number of sources of error for the instrument.In this thesis a new method for counting particles based on light microscopywith image analysis is proposed. It has proven to be a fast and effective methodthat eliminates the sources of error of the previously described methods. Thenew method correlates very well with manual analysis which is used as a refer-ence method throughout this study. Size estimation of particles and detectionof metallic particles has also shown to be possible with the current image analy-sis setup. With more advanced software and analysis instrumentation, the imageanalysis method could be further developed to a decision based machine allowingfor declarations about which wear mode is occurring in a mechanical system.

Place, publisher, year, edition, pages
2011. , 91 p.
Keyword [en]
contaminants in oil, counting particles, image analysis, oil condition monitoring, sizing particles, ISO4406, wear particles
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:liu:diva-68750ISRN: LITH-ISY-EX--11/4467--SEOAI: diva2:420518
Subject / course
Computer Vision Laboratory
2011-06-01, Algoritmen, Linköping University, Department of Electrical Engineering, Computer Vision Laboratory, SE-581 83 Linköping, Sweden, 08:15 (Swedish)
Available from: 2011-06-08 Created: 2011-06-01 Last updated: 2011-06-08Bibliographically approved

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