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Statistical monitoring of autocorrelated simple linear profiles based on principal components analysis

Akhavan Niaki, S. T ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1080/03610926.2013.835417
  3. Publisher: Taylor and Francis Inc , 2015
  4. Abstract:
  5. In this article, a transformation method using the principal component analysis approach is first applied to remove the existing autocorrelation within each profile in Phase I monitoring of autocorrelated simple linear profiles. This easy-to-use approach is independent of the autocorrelation coefficient. Moreover, since it is a model-free method, it can be used for Phase I monitoring procedures. Then, five control schemes are proposed to monitor the parameters of the profile with uncorrelated error terms. The performances of the proposed control charts are evaluated and are compared through simulation experiments based on different values of autocorrelation coefficient as well as different shift scenarios in the parameters of the profile in terms of probability of receiving an out-of-control signal
  6. Keywords:
  7. Simple linear profile ; Statistical process control ; Autocorrelation ; Linear transformations ; Mathematical transformations ; Autocorrelation coefficient ; Linear profiles ; Out-of-control signals ; Phase I ; Statistical monitoring ; Transformation methods ; Uncorrelated errors ; Principal component analysis
  8. Source: Communications in Statistics - Theory and Methods ; Volume 44, Issue 21 , Nov , 2015 , Pages 4454-4475 ; 03610926 (ISSN)
  9. URL: http://www.tandfonline.com/doi/full/10.1080/03610926.2013.835417