Boiling anomaly detection by various signal characterization methods

In order to detect anomalies in the early stage for complex dynamical systems like nuclear power plants, it is important to characterize various statistical features of the data acquired in normal operating condition. In this paper, concept of hierarchical anomaly monitoring method is outlined, which is based on the diversification principle. In addition to usual time and frequency domain analysis (FFT, APDF, MAR-SPRT), other analysis (wavelet, fractal, etc.) are performed. As soon as any inconsistency arises in the results of the analysis on the upper level, a thorough analysis is initiated. A comparison among these methods is performed and the efficiency of the diversification approach has been demonstrated through simulated boiling anomalies in nuclear reactors. (authors)