Machine Learning Based Classification of Wear Debris

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25510%2F12%3A39894538" target="_blank" >RIV/00216275:25510/12:39894538 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Machine Learning Based Classification of Wear Debris
Original language description
The wear debris of various engineering equipment (such as combustion engines, gearboxes, etc.) consists of particles of metal which can be obtained in lubricants used in such machine parts. The analysis the the wear particles is very important for earlydetection and prevention of failures in engineering equipment. The analysis is often done through the classi cation of individual wear particles obtained by analytical ferrography. In this paper, we present a study of feature extraction methods for a classi cation of the wear particles based on visual similarity (using supervised machine learning). The fi rst contribution of the paper is the comparison of nine selected feature types in the context of three state-of-the-art learning models. The second contribution is the large public database of binary images of particles which can be used for further experiments. The paper describes the dataset, methods of classi cation, demonstrates experimental results, and draws conclusions.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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