Wear particle analysis based on self-organizing clusters

Research output: Contribution to journalReview articlepeer-review


The focus of this paper is integration of system process information obtained through an image-processing system with an evolving knowledge database to improve the accuracy and predictability of wear particle analysis. We describe the process of deriving such parameters from images of wear particles using an image-processing system. The objective is to classify this wear particle information for possible cluster analysis via self-organizing maps. This can be achieved using relationship measurements among corresponding attributes of various measurements for wear particle analysis. As a result, it helps in predicting wear failure modes in engines and other machinery. Finally, simulations are performed to estimate the efficiency of the proposed system along with visualization techniques that help the viewer in understanding and utilizing these relationships that enable accurate diagnostics and provide the in-depth data needed to support results.

Original languageEnglish
Pages (from-to)282-287
Number of pages6
JournalInternational Journal of Robotics and Automation
Issue number4
Publication statusPublished - 2006


  • Associations
  • Kohonen networks
  • Self-organizing clusters
  • Wear particle analysis

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Modelling and Simulation
  • Mechanical Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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