APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR THE DEVELOPMENT OF A SIGNAL MONITORING-SYSTEM

A prototype of a Signal Monitoring System (SMS) utilizing artificial n eural networks is developed in this work. The prototype system is uniq ue in: 1) its utilization of state-of-the-art technology in pattern re cognition such as the Adaptive Resonance Theory family of neural netwo rks, and 2) the Integration of neural network results of pattern recog nition and fault identification databases. The system is developed in an X-windows environment that offers an excellent Graphical User Inter face (GUI). Motif software is used to build the GUI. The system is use r-friendly, menu-driven, and allows the user to select signals and par adigms of interest. The system provides the status or condition of the signals tested as either normal or faulty. In the case of faulty stat us, SMS, through an integrated database, identifies the fault and indi cates the progress of the fault relative to the normal condition as we ll as relative to the previous tests. Nuclear reactor signals from an Experimental Breeder Reactor are analyzed to closely represent actual reactor operational data. The signals are both measured signals collec ted by a Data Acquisition System as well as simulated signals.