Application of artificial neural networks for the development of a signal monitoring system

A prototype of a Signal Monitoring System (SMS) utilizing artificial neural networks is developed in this work. The prototype system is unique in: 1) its utilization of state-of-the-art technology in pattern recognition such as the Adaptive Resonance Theory family of neural networks, and 2) the Integration of neural network results of pattern recognition and fault identification databases. The system is developed in an X-windows environment that offers an excellent Graphical User Interface (GUI). Motif software is used to build the GUI. The system is user-friendly, menu-driven, and allows the user to select signals and paradigms of interest. The system provides the status or condition of the signals tested as either normal or faulty. In the case of faulty status, SMS, through an integrated database, identifies the fault and indicates the progress of the fault relative to the normal condition as well 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 collected by a Data Acquisition System as well as simulated signals.