Model-based diagnosis of special causes in statistical process control

Kevin Dooley, J. Anderson, X. Liu

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Industry has recognized that effective use of automated diagnostic software can greatly enhance process quality and productivity. Simultaneously, significant advances have been made in the technologies of process modelling, using techniques such as neural networks, regression methods, and various analytical approaches. Here we will present a simple method to perform model-based diagnosis. The method is simple to implement, intuitively appealing, and requires information that should be standardly available. The method requires as input current process data, set-point information, and a predictive process model, and outputs a table of diagnostic scores which indicate the likelihood of a particular factor being the cause of an observed special cause on a statistical process control chart.

Original languageEnglish (US)
Pages (from-to)1609-1616
Number of pages8
JournalInternational Journal of Production Research
Volume35
Issue number6
StatePublished - Jun 1997
Externally publishedYes

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Statistical process control
Productivity
Neural networks
Industry
Diagnostics
Control charts

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Management Science and Operations Research

Cite this

Model-based diagnosis of special causes in statistical process control. / Dooley, Kevin; Anderson, J.; Liu, X.

In: International Journal of Production Research, Vol. 35, No. 6, 06.1997, p. 1609-1616.

Research output: Contribution to journalArticle

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