Dynamic kriging based fault detection and diagnosis approach for nonlinear noisy dynamic processes

Abstract

This paper presents a hybrid approach to improve data-based Fault Detection and Diagnosis (FDD). It is applicable to nonlinear dynamic noisy processes, operated under time-varying inputs. The method is based on the combination of kriging models and Pattern Recognition Techniques. A set of Multivariate Dynamic Kriging-based predictors (MDKs) is built and used to estimate the process dynamic behavior, while static kriging models are used to smooth the eventually noisy process outputs. The estimated and the actual smoothed outputs are compared, taking advantage of the higher capacity of the residual patterns generated in this way to characterize the process state. The performance of the method is illustrated through its application to a well-known benchmark case study, for which the FDD performance has been significantly improved. This improvement is consistently maintained in different dynamic operating conditions and faulty situations, including scenarios with modified fault severities and fault styles.