A Laplacian spectral method in phase I analysis of profiles

Francisco Moura Neto and Maysa S. De Magalhães

Applied Stochastic Models in Business and Industry, 2012, vol. 28, issue 3, 251-263

Abstract: The statistical process control of certain complex production processes or products rely on the monitoring of profiles. In this work, we consider a spectral‐based method to distinguish between outlier and nonoutlier observations to establish profile parameters. The proposed method is based on the spectral theory of the Laplacian of a graph, more specifically, a Laplacian eigenmap. The graph is constructed by considering each profile as a node, and by adjusting edge weights on the graph to reflect the resemblance between profiles. Laplacian eigenmaps have been used to represent data, to reduce data dimension, and for clustering. The proposed method can be used for complex profiles because it does not require the function between the dependent and independent variables. We apply the proposed method to an artificially generated data set from a nonlinear profile and to a wood board production problem. The results are compared with existing methods in the literature and show that the proposed method performs satisfactorily. Copyright © 2012 John Wiley & Sons, Ltd.

Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1002/asmb.1916

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:28:y:2012:i:3:p:251-263

Access Statistics for this article

More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().