Wear Particle Texture Analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Wear particle analysis is used for health monitoring of a machine to identify possible failure modes resulting from various machine components. One of the initial phases of analysis typically involves a microscopic examination of particles extracted from the lubrication system. Experts in the field (Tribologists) utilize this important information from these examinations to monitor the operation of the machine and ensure efficiency, safety, and economy of the operation. This extracted information has been characterized by six morphological features of particle shape, edge curvature, size, color, thickness ratio, and surface texture. These features are the building blocks in the creation of image analysis systems to supplement the wear judgments made by experts who use traditional methods of diagnostics. The paper investigates one of the six morphological features related to the surface texture classification of wear particles. Image processing procedures of line/edge detection and Artificial Neural Networks are used to recognize four texture types of smooth, pitted, striations, and rough. An accuracy classification rate of approximately 98.9% has been achieved and is shown by a confusion matrix.

Original languageEnglish
Title of host publication2019 3rd International Conference on Imaging, Signal Processing and Communication, ICISPC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages67-72
Number of pages6
ISBN (Electronic)9781728136639
DOIs
Publication statusPublished - Jul 2019
Event3rd International Conference on Imaging, Signal Processing and Communication, ICISPC 2019 - Singapore, Singapore
Duration: Jul 27 2019Jul 29 2019

Publication series

Name2019 3rd International Conference on Imaging, Signal Processing and Communication, ICISPC 2019

Conference

Conference3rd International Conference on Imaging, Signal Processing and Communication, ICISPC 2019
Country/TerritorySingapore
CitySingapore
Period7/27/197/29/19

Keywords

  • Artificial Neural Networks
  • image processing
  • texture classification
  • wear particles

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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