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TECHNICAL PAPERS

Model Based Statistical Process Control for Continuous Systems

[+] Author and Article Information
Peter J. Linstrom

Oso Tecnologies, Inc., Columbia, MD 21044

O. Andreas Asbjo̸rnsen

Department of Mechanical Engineering, Division of Thermal Energy and Hydro-Power, University of Trondheim, The Norwegian Institute of Technology, N-7034 Trondheim, Norway

J. Dyn. Sys., Meas., Control 118(2), 283-289 (Jun 01, 1996) (7 pages) doi:10.1115/1.2802316 History: Received August 01, 1993; Revised November 01, 1994; Online December 03, 2007

Abstract

Statistical process control (SPC) is a set of methodologies for signaling the presence of undesired sources of variation in manufacturing processes. SPC methods for continuous processes may be developed by using stochastic models which do not assume that successive observations are independent. A method for applying SPC to continuous processes is presented. This method incorporates a computationally efficient procedure for the on-line identification and estimation of autoregressive with exogenous inputs (ARX) models. Two examples illustrating the method for SPC monitoring are presented.

Copyright © 1996 by The American Society of Mechanical Engineers
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