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Surface Feature Recognition of Wear Debris

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AI 2002: Advances in Artificial Intelligence (AI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2557))

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Abstract

Microscopic wear debris is produced in all machines containing moving parts in contact. The debris (particles), transported by a lubricant from wear sites; carry important information relating to the condition of the machinery. This information is classified by compositional and six morphological attributes of particle size, shape, edge details, color, thickness ratio, and surface texture. The paper describes an automated system for surface features recognition of wear particles by using artificial neural networks. The aim is to classify these particles according to their morphological attributes and by using the information obtained, to predict wear failure modes in engines and other machinery. This approach will enable the manufacturing industry to improve quality, productivity and economy. The procedure reported in this paper is based on gray level cooccurrence matrices, that are used to train a feed-forward neural network classifier in order to distinguish among seven different patterns of wear particles. The patterns are: smooth, rough, striations, holes, pitted, cracked, and serrated. An accuracy classification rate of 94.6% has been achieved and is shown by a confusion matrix.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, United Arab Emirates University, P.O. Box: 17555, Al-Ain, U.A.E.

    Mohammad Shakeel Laghari

Authors
  1. Mohammad Shakeel Laghari
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Editor information

Editors and Affiliations

  1. Australian Defence Force Academy, University of New South Wales, ACT 2600, Canberra, Australia

    Bob McKay

  2. Computer Science Laboratory, Australian National University, RSISE Building, ACT 0200, Canberra, Australia

    John Slaney

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© 2002 Springer-Verlag Berlin Heidelberg

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Laghari, M.S. (2002). Surface Feature Recognition of Wear Debris. In: McKay, B., Slaney, J. (eds) AI 2002: Advances in Artificial Intelligence. AI 2002. Lecture Notes in Computer Science(), vol 2557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36187-1_55

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  • DOI: https://doi.org/10.1007/3-540-36187-1_55

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00197-3

  • Online ISBN: 978-3-540-36187-9

  • eBook Packages: Springer Book Archive

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