Machine Learning Based Classification of Wear Debris

Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning Based Classification of Wear Debris

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine Learning Based Classification of Wear Debris

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Machine Graphics and Vision

ISSN

1230-0535

e-ISSN

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Svazek periodika

2012

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

11

Strana od-do

1-11

Kód UT WoS článku

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EID výsledku v databázi Scopus

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