Detail publikace

Analysis Wear Debris Through Classification

Originální název

Analysis Wear Debris Through Classification

Anglický název

Analysis Wear Debris Through Classification

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

BibTex


@inproceedings{BUT76367,
  author="Roman {Juránek} and Stanislav {Machalík} and Pavel {Zemčík}",
  title="Analysis Wear Debris Through Classification",
  annote="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.",
  address="Springer Verlag",
  booktitle="Proceedings of Advanced Concepts of Inteligent Vision Systems (ACIVS 2011)",
  chapter="76367",
  edition="Lecture Notes in Computer Science",
  howpublished="print",
  institution="Springer Verlag",
  year="2011",
  month="august",
  pages="273--283",
  publisher="Springer Verlag",
  type="conference paper"
}