Publication detail

Research of Imgae Features for Classification of Wear Debris

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"
}