Analysis of Wear Debris through Classification

  • Roman Juránek
  • Stanislav Machalík
  • Pavel Zemčík
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6915)

Abstract

This paper introduces a novel method of wear debris analysis through classification of the particles based on machine learning. Wear debris consists of particles of metal found in e.g. lubricant oils used in engineering equipment. Analytical ferrography is one of methods for wear debris analysis and it is very important for early detection or even prevention of failures in engineering equipment, such as combustion engines, gearboxes, etc. The proposed novel method relies on classification of wear debris particles into several classes defined by the origin of such particles. Unlike the earlier methods, the proposed classification approach is based on visual similarity of the particles and supervised machine learning. The paper describes the method itself, demonstrates its experimental results, and draws conclusions.

Keywords

IEEE Computer Society False Negative Rate Local Binary Pattern Wear Debris Sample Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Roman Juránek
    • 1
  • Stanislav Machalík
    • 2
  • Pavel Zemčík
    • 1
  1. 1.Graph@FIT Faculty of Information TechnologyBrno University of TechnologyCzech Republic
  2. 2.University of PardubiceCzech Republic

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