Gaussian Mixture Densities for Classification of Nuclear Power Plant Data
In this paper we are concerned with the application of learning algorithms to the classification of reactor states in nuclear plants. Two aspects must be considered: (1) some types events (e.g., abnormal or rare) will not appear in the data set, but the system should be able to detect them, (2) not only classification of signals but also their interpretation are important for nuclear plant monitoring. We address both issues with a mixture of mixtures of Gaussians in which some parameters are shared to reflect the similar signals observed in different states of the reactor. An EM algorithm for these shared Gaussian mixtures is presented.  Experimental results on nuclear plant data demonstrate  the advantages of the proposed approach with respect to the above two points.