Fault diagnosis in multivariate control charts using artificial neural networks

Akhavan Niaki, S. T ; Sharif University of Technology | 2005

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  1. Type of Document: Article
  2. DOI: 10.1002/qre.689
  3. Publisher: 2005
  4. Abstract:
  5. Most multivariate quality control procedures evaluate the in-control or out-of-control condition based upon an overall statistic, like Hotelling's T2. Although T2 is optimal for finding a general shift in mean vectors, it is not optimal for shifts that occur for some subset of variables. This introduces a persistent problem in multivariate control charts, namely the interpretation of a signal that often discourages practitioners in applying them. In this paper, we propose an artificial neural network based model to diagnose faults in out-of-control conditions and to help identify aberrant variables when Shewhart-type multivariate control charts based on Hotelling's T2 are used. The results of the model implementation on two numerical examples and one case of real world data are encouraging. Copyright © 2005 John Wiley & Sons, Ltd
  6. Keywords:
  7. Diagnosis ; Mathematical models ; Neural networks ; Quality control ; Statistical process control ; Fault diagnosis ; Multivariate quality control ; Multuvartiate control charts ; Control theory
  8. Source: Quality and Reliability Engineering International ; Volume 21, Issue 8 , 2005 , Pages 825-840 ; 07488017 (ISSN)
  9. URL: https://onlinelibrary.wiley.com/doi/10.1002/qre.689