CHARACTERIZATION OF ANOMALIES BY APPLYING METHODS OF FRACTAL ANALYSIS

Fractal analysis is applied in a variety of research fields to charact erize nonstationary data. Here, fractal analysis is used as a tool of characterization in time series. The fractal dimension is calculated b y Higuchi's method, and the effect of small data size on accuracy is s tudied in detail. Three types of fractal-based anomaly indicators are adopted: (a) the fractal dimension, (b) the error of the fractal dimen sion, and (c) the chi-square value of the linear fitting of the fracta l curve in the wave number domain. Fractal features of time series can be characterized by introducing these three measures. The proposed me thod is applied to various simulated fractal time series with ramp, ra ndom, and periodic noise anomalies and also to neutron detector signal s acquired in a nuclear reactor. Fractal characterization can successf ully supplement conventional signal analysis methods especially if non stationary and non-Gaussian features of the signal become important.