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Design of a Statistical Control Chart for Simultaneous Monitoring and Fault Isolation of Mean Vector and Covariance Matrix of Multivariate Multistage Processes, M.Sc. Thesis Sharif University of Technology ; Akhavan Niaki, Taghi (Supervisor)
Abstract
In modern industries, multivariate multistage auto-correlated processes are widely used to ensure productivity and product quality. Interconnections between work stations bring a challenging task in detecting various shifts and identifying their root causes. In addition, simultaneous monitoring process mean and variability with single control chart methods has gained considerable attention throughout these years. In this article, a double-max multivariate exponentially weighted moving average (DM-MEWMA) chart is proposed based on two novel statistics to monitor the parameters of multivariate multistage auto-correlated processes jointly. Prior knowledge of variation propagation has been used...
Cataloging briefDesign of a Statistical Control Chart for Simultaneous Monitoring and Fault Isolation of Mean Vector and Covariance Matrix of Multivariate Multistage Processes, M.Sc. Thesis Sharif University of Technology ; Akhavan Niaki, Taghi (Supervisor)
Abstract
In modern industries, multivariate multistage auto-correlated processes are widely used to ensure productivity and product quality. Interconnections between work stations bring a challenging task in detecting various shifts and identifying their root causes. In addition, simultaneous monitoring process mean and variability with single control chart methods has gained considerable attention throughout these years. In this article, a double-max multivariate exponentially weighted moving average (DM-MEWMA) chart is proposed based on two novel statistics to monitor the parameters of multivariate multistage auto-correlated processes jointly. Prior knowledge of variation propagation has been used...
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