Detection of Gross Errors in Nonlinearly Constrained Data: A Case Study

The MIMT algorithm previously developed for gross error detection in linearly constrained systems was extended to nonlinear systems. The algorithm was tested by means of computer simulation using data from an industrial grinding circuit. The overall performance of the algorithm on the nonlinear system was found to be comparable to that exhibited on a linear system of approximately the same size. The algorithm correctly detected approximately 80% of all systematic errors in the data and achieved an average reduction in total error of more than 60%. The detection rate for the more significant (gross) systematic errors was approximately 90%. These results represent the first detailed performance evaluation of a gross error detection algorithm applied to a nonlinear system of industrial significance.