Signal Validation Using an Adaptive Neural Fuzzy Inference System

An adaptive neural fuzzy inference system modeling technique is introduced for sensor and associated instrument channel calibration validation. This method uses an inferential-modeling technique after a genetic algorithm search is used to empirically determine the appropriate combinations of input variables to optimally model each signal to be monitored. These variables are used as input to a fuzzy inference system that is trained to estimate the monitored signals. The estimates are compared with the actual signals, and a statistical decision technique known as the sequential probability ratio test is used to detect sensor anomalies. The sensor fault detection system is demonstrated using data supplied from Florida Power Corporation’s Crystal River Unit 3 nuclear power generating station.