A Total Least Squares Approach of Pattern Recognition for Model Based Fault Detection (original) (raw)

Fuzzy and non-parametric theoretic-decision methods of pattern recognition are used for the model based fault detection via parameter estimation. The present approach uses the concepts of fuzzy weighted distance and of total least-squares, on one hand, and the outputs of an optimized procedure of fuzzy clustering. on the other, to construct and to improve the performances of a linear trainable classifier of the least-squares minimum distance type. Simulation results regarding the diagnosis of a d.c. motor-pump-pipe process studied by means of numerical simulation are included.