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.

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