Univerzita Pardubice Dopravní fakulta Jana Pernera

Univerzita Pardubice

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Publikace detail

Modern Techniques of Wear Debris Analysis

Autoři: Machalík Stanislav

Rok: 2008

Druh publikace: článek v odborném periodiku

Název zdroje: Sovremennyj naučnyj vestnik

Název nakladatele: Rusnaučkniga

Místo vydání: Belgorod

Strana od-do: 82-85

Tituly:

Jazyk	Název	Abstrakt	Klíčová slova
cze	Modern Techniques of Wear Debris Analysis	Lubricants used in mechanical parts must accomplish contradictory requirements on their function in many cases and, at the same time, they must often work in extreme conditions with longer service life. Increasing the reliability and economy of machine use is closely connected to monitoring the condition and state of technical parts of used lubricant with the purpose of diagnostics. Computer image analysis of wear particles is a useful supporting tool for detail analysis of oil samples. Presently, laboratory methods of analysing the individual elements under a microscope are used most frequently. Modern methods, including machine learning, provide possibilities of automatization of wear debris analysis. The goal of this paper is to outline the possibilities of improvement in image analysis with the help of automatic evaluation of wear particles by using modern methods of artificial intelligence.
eng	Modern Techniques of Wear Debris Analysis	Lubricants used in mechanical parts must accomplish contradictory requirements on their function in many cases and, at the same time, they must often work in extreme conditions with longer service life. Increasing the reliability and economy of machine use is closely connected to monitoring the condition and state of technical parts of used lubricant with the purpose of diagnostics. Computer image analysis of wear particles is a useful supporting tool for detail analysis of oil samples. Presently, laboratory methods of analysing the individual elements under a microscope are used most frequently. Modern methods, including machine learning, provide possibilities of automatization of wear debris analysis. The goal of this paper is to outline the possibilities of improvement in image analysis with the help of automatic evaluation of wear particles by using modern methods of artificial intelligence.	image analysis, wear debris analysis, machine learning methods, analytical ferrography