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Proceedings Paper

Inspection of wear particles in oils by using a fuzzy classifier
Author(s): Jari J. Hamalainen; Petri Enwald
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Paper Abstract

The reliability of stand-alone machines and larger production units can be improved by automated condition monitoring. Analysis of wear particles in lubricating or hydraulic oils helps diagnosing the wear states of machine parts. This paper presents a computer vision system for automated classification of wear particles. Digitized images from experiments with a bearing test bench, a hydraulic system with an industrial company, and oil samples from different industrial sources were used for algorithm development and testing. The wear particles were divided into four classes indicating different wear mechanisms: cutting wear, fatigue wear, adhesive wear, and abrasive wear. The results showed that the fuzzy K-nearest neighbor classifier utilized gave the same distribution of wear particles as the classification by a human expert.

Paper Details

Date Published: 23 November 1994
PDF: 9 pages
Proc. SPIE 2249, Automated 3D and 2D Vision, (23 November 1994); doi: 10.1117/12.196085
Show Author Affiliations
Jari J. Hamalainen, VTT Automation (Finland)
Petri Enwald, VTT Manufacturing Technology (Finland)


Published in SPIE Proceedings Vol. 2249:
Automated 3D and 2D Vision
Rolf-Juergen Ahlers; Donald W. Braggins; Gary W. Kamerman, Editor(s)

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