Identification of Surface Characteristics from Large Samples

R. Kovacevic, Y. M. Zhang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Surface roughness characteristics have been modelled by autoregressive moving average (ARMA) models. Frequently, extra-large samples from the surface are available. Due to the non-linearity and the computational burden dependence on sample size, the available data can not be sufficiently utilized to fit ARMA models in most cases. In an attempt to sufficiently employ the available data, an innovative ARMA identification approach is presented. The computational burden of this approach is nearly independent of the sample size. The accuracy ratio between the present approach and the non-linear least squares algorithm is determined. Both simulation and application have been conducted to confirm its effectiveness.

Original languageEnglish
Pages (from-to)275-284
Number of pages10
JournalProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Issue number4
StatePublished - Jul 1992

ASJC Scopus subject areas

  • Mechanical Engineering


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