Wavelet analysis and structural entropy based intelligent classification method for combustion engine cylinder surfaces

S. Nagy, Levente Solecki

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Structural entropy is a good candidate for characterizing roughness of surfaces as it is sensitive not only to the general shape of the surface, but also to the rate of the high and low surface points. Wavelet analysis of the surface can separate the larger-scale behavior from the fine details, and together with the structural entropy it can define a behavior profile for the surface which is typically slightly different for new and for worn tribological surfaces. Also it is important to know whether the method of the surface scan has influence on the structural entropy’s wavelet analysis profile, as the lower cost images based on silicone replica and optical scanner have less sensitivity than the higher cost contact scan of the prepared real surface parts. An intelligent fuzzy classification scheme is introduced to characterize surfaces according to both their degree of wear and method of the surface measurement. The basis of the classification is the structural entropies of the original and the first wavelet transform of the height scan of the new and worn surfaces.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages127-138
Number of pages12
DOIs
Publication statusPublished - Jan 1 2019

Publication series

NameStudies in Computational Intelligence
Volume794
ISSN (Print)1860-949X

Keywords

  • Fuzzy classification
  • Rényi entropy
  • Surface classification
  • Wavelet analysis

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Nagy, S., & Solecki, L. (2019). Wavelet analysis and structural entropy based intelligent classification method for combustion engine cylinder surfaces. In Studies in Computational Intelligence (pp. 127-138). (Studies in Computational Intelligence; Vol. 794). Springer Verlag. https://doi.org/10.1007/978-3-030-01632-6_9