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MACHALÍK, S. JURÁNEK, R. ZEMČÍK, P.
Original Title
Research of Imgae Features for Classification of Wear Debris
Type
journal article - other
Language
English
Original Abstract
The wear debris of various engineering equipment (such as combustion engines, gearboxes, etc.) consists of particles of metal which can be obtained from lubricants used in such machine parts. The analysis the wear particles is very important for early detection and prevention of failures in engineering equipment. The analysis is often done through classification of individual wear particles obtained by analytical ferrography. In this paper, we present a study of feature extraction methods for a classification of the wear particles based on visual similarity (using supervised machine learning). The main contribution of the paper is the comparison of nine selected feature types in the context of three state-of-the-art learning models. Another contribution is the large public database of binary images of particles which can be used for further experiments.
Keywords
Wear Debris, Classification, Supervised Machine Learning, SVM, Linear Regression,Features, PCA, HOG, LBP
Authors
MACHALÍK, S.; JURÁNEK, R.; ZEMČÍK, P.
RIV year
2012
Released
1. 2. 2012
ISBN
1230-0535
Periodical
Machine Graphics and Vision
Year of study
20
Number
1
State
Republic of Poland
Pages from
479
Pages to
493
Pages count
15
BibTex
@article{BUT91470, author="Stanislav {Machalík} and Roman {Juránek} and Pavel {Zemčík}", title="Research of Imgae Features for Classification of Wear Debris", journal="Machine Graphics and Vision", year="2012", volume="20", number="1", pages="479--493", issn="1230-0535" }