Neural and Fuzzy Transient Classification Systems: General Techniques and Applications in Nuclear Power Plants

Abstract

In this chapter the problem of identing events in dynamic processes (e.g., faults, anomalous behaviours, etc.) is tackled with soft computing techniques aimed at the classification of the process transients generated by such events. A review of previous work is followed by a discussion of several alternative designs and models which employ both fuzzy and neural systems. These have been developed during an ongoing reserch program which was initiated by the need of finding new principled methods to perform alarm structuring/suppression in a nuclear power plant alarm system. This initial goal was soon expanded beyond alarm handling, to include diagnostic tasks in general. The application of these systems to domains other than NPPs was also taken into special consideration. A systematic study was carried out with the aim of comparing alternative neural network designs and models. Four main approaches have been investigated: radial basis function (RBF) neural networks and cascade-RBF neural networks combined with fuzzy clustering, self-organizing map neural networks, and recurrent neural networks. The main evaluation criteria adopted were: identification accuracy, reliability (i.e., correct recognition of an unknown event as such), robustness (to noise and to changing initial conditions), and real time performance. A series of initial tests on a small set of BWR transients was recently followed by more advanced tests on PWR transients corresponding to various occurrences of rapid load rejection events (plant islanding). The chapter is closed by a discussion of open issues and future directions for research and applications.