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Monitoring of Proportional‐Integral Controlled Processes using a Bayesian Time Series Analysis Method
Author(s) -
Vanli O. Arda
Publication year - 2014
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1555
Subject(s) - control chart , bayesian probability , computer science , chart , series (stratigraphy) , statistical process control , time point , time series , process (computing) , shewhart individuals control chart , change detection , exponential function , control theory (sociology) , statistics , control (management) , mathematics , ewma chart , artificial intelligence , machine learning , paleontology , mathematical analysis , philosophy , aesthetics , biology , operating system
Recently, there has been interest in applying statistical process monitoring methods to processes controlled with feedback controllers in order to eliminate assignable causes and achieve reduced overall variability. In this paper, we propose a Bayesian change‐point method to monitor processes regulated with proportional‐integral controllers. The approach is based on fitting an exponential rise model to the control input actions in response to a step shift and employs a change‐point method to detect the change. Simulation studies show that the proposed method has better run‐length performance in detecting step shifts in controlled processes than Shewhart chart on individuals and special‐cause chart on residuals of time series model. Copyright © 2013 John Wiley & Sons, Ltd.