Monitoring for changes in the nature of stochastic textured surfaces

Anh Tuan Bui, Daniel W. Apley*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

We propose an approach for monitoring general global changes in the nature of stochastic textured surfaces using streams of high-dimensional images or related profile data. Stochastic textured surfaces are fundamentally different than the profiles and images that are the focus of most prior profile monitoring works. We represent normal in-control behavior by using supervised learning algorithms to implicitly characterize the joint distribution of the stochastic textured surface pixels. Based on this characterization, we develop a control chart monitoring statistic using likelihood-ratio principles to quantify and detect changes in the stochastic nature of the surfaces, relative to the in-control surfaces. Unlike methods that look for changes in specific predefined features, our approach can detect very general changes in the nature of the textured surfaces. We demonstrate the implementation and effectiveness of the approach with a real textile example and a simulation example.

Original languageEnglish (US)
Pages (from-to)363-378
Number of pages16
JournalJournal of Quality Technology
Volume50
Issue number4
DOIs
StatePublished - 2018

Keywords

  • Anomaly detection
  • Fault detection
  • Generalized likelihood-ratio test (GLRT)
  • Markov random field
  • One-class classification
  • Statistical process control (SPC)
  • Supervised learning

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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