ROBUST CLASSIFICATION OF SYSTEMATIC MEASUREMENT ERRORS

LLANOS CLAUDIA; MARONNA RICARDO; SANCHEZ MABEL; CHAVEZ GALLETTI ROBERTO

San Francisco

Congreso; 2016 AIChE Annual Meeting; 2016

American Institute of Chemical Engineers

In this work, a new strategy for the robust classification of systematic measurements errors is presented. The DR stage is based on the SiM. The identification stage uses the Measurements Test (MT) to detect outliers or suspicious variables. When the MT is applied to the last two observations of the moving window, two cases may arise. If the statistical test of only the penultimate measurement is greater than its critical value, an outlier has been identified. In contrast if the statistical values of the two observations are greater than the critical ones, then the next measurements are saved to make a robust linear regression. The relation between the estimated slope and the variance of the observations allows distinguishing between biases and drifts.Application results of the methodology for linear and nonlinear benchmarks, extracted from the literature, are used to evaluate its performance. Three cases are analyzed for different lengths of the data horizon. The first one involves the presence of outliers and biases; the second one considers that measurements may contain outliers and drifts, and the last one studies the possible existence of all the systematic observation errors. The performance of the proposed methodology is measured in terms of the Percentage of Right Classification for each type of systematic error and a global index. Results show that there is a window length which allows obtaining the best classification of the simulated errors.