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A plant-wide oscillation in a chemical process often has an impact on product quality and running costs and there is, thus, a motivation for automated diagnosis of the source of such a disturbance. This brief describes a method of analyzing data from routine operation to locate the root cause oscillation in a dynamic system of interacting control loops and to distinguish it from propagated secondary oscillations. The novel concept is the application of a nonlinearity index that is strongest at the source. The index is large for the nonsinusoidal oscillating time trends that are typical of the output of a control loop with a limit cycle caused by nonlinearity. It is sensitive to limit cycles caused both by equipment and by process nonlinearity. The performance of the index is studied in detail and default settings for the parameters in the algorithm are derived so that it can be applied in a large scale setting such as a refinery or petrochemical plant. Issues arising from artifacts in the nonlinearity test when applied to strongly cyclic data have been addressed to provide a robust, reliable and practical method. The technique is demonstrated with three industrial case studies.
Date of Publication: May 2005