Empirical process modeling technique for signal validation

Abstract

Some techniques for fault detection involve the comparison of measured process signals with independent estimates. The prediction of process variables can be achieved either by physical or empirical modeling of a plant subsystem. An automated procedure for generating empirical process models is developed here. Independent prediction of critical signals is required for consistency checking of instrument outputs, for their degradation monitoring and for isolating common-mode failures. The steady-state empirical models are developed using data from different steady-state conditions. Signal anomaly is identified by comparing the error between the model-based prediction and the actual measurement with a fuzzy function (curve) utilizing the signal tolerance as a threshold. In the event a signal is declared as failed, the predicted estimate is used as input to a control/safety system or for display to an operator. Application of the methodology to signal validation using operational data from a commercial PWR and the EBR-II is presented.