A Calibration and Estimation Filter for Multiply Redundant Measurement Systems

Abstract

An adaptive filter has been developed for calibration and estimation in multiply redundant measurement systems. The filter is structured in the framework of a fault detection and isolation (FDI) methodology where the decisions are made on the basis of consistencies among all redundant measurements. The consistent measurements are calibrated on-line to compensate for their errors. An estimate of the measured variable is obtained as a weighted average of the calibrated measurements where the individual weights are adaptively updated on-line on the basis of the respective a posteriori probabilities of failure instead of being a priori fixed. The calibration and estimation algorithm is suitable for real-time applications using commercially available microcomputer systems, and has been verified by on-line demonstrations in an operating nuclear reactor.