Publication

Abstract : A new method to detect the existence of biased measured variables in dynamic processes is presented. Hence, this work presents a new Dynamic Global Test (DGT) and test procedure for dynamic gross error detection (GED) that brings to light certain of its attributes which have not hitherto (to our knowledge) been presented in GED literature. Recognition of these attributes leads to a scheme that enables identification of the type of biased measurement (e.g. flow or level). This approach is not computationally intensive and is applicable in the case of process leaks and multiple biased variables. Simulation results for the identification of the type of biased measurement (e.g. flow or level) and the estimation of the time of occurrence (ETOC) are given. The performance study in this work specifically varied the size of measurement bias (δi), the bias location (i), the bias true time of occurrence (TTOC), the significance level (α), and the sample size (N). This study shows the proposed approach to be accurate in identifying the type of biased variable and its TTOC. The performance of the proposed scheme improves as N and δi increase.