Publication Details
Research of Image Features for Classification of Wear Debris
Juránek Roman, Ing., Ph.D. (DCGM)
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)
and others
Wear Debris, Classification, Supervised Machine Learning, SVM, Linear
Regression,Features, PCA, HOG, LBP
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.
@article{BUT91470,
author="Stanislav {Machalík} and Roman {Juránek} and Pavel {Zemčík}",
title="Research of Image Features for Classification of Wear Debris",
journal="Machine Graphics and Vision",
year="2012",
volume="20",
number="1",
pages="479--493",
issn="1230-0535"
}