A methodology is presented that makes use of wavelet bases as a means for computing the probability density functions associated with different system states in a nuclear environment. Multiresolution analysis is coupled with multivariate statistics to form a tool powerful enough to estimate multidimensional density functions from highly correlated system variables. Wavelets that adapt well to local characteristics of rapidly varying functions are employed as building blocks of the proposed approach. The identification of different system states is a first step toward developing a reference pattern database that may be used for identifying future abnormal behavior. The methodology is illustrated by monitoring parameters from two nuclear reactor systems. In the first case, data from the secondary heat transfer system of the Monju fast breeder reactor have been used, while in the latter, neutron noise from an experimental reactor facility has been analyzed to detect bubble flow. The results obtained exhibit the potential value of the proposed scheme, which appears capable of distinguishing among various steady-state and transient conditions.
