Supervision and Safety of Complex Systems

Chapter 11A Dynamic Learning-based Approach to theSurveillance and Monitoring of SteamGenerators in Prototype Fast Reactors ¹

 

 

 

This research focuses on the surveillance and monitoring of evolving systems [ANG 04, LUG 11b, KAS 07, ANG 10, LUG 11a] using learning methods and dynamic classification. Like any evolving system, it changes from one mode to another suddenly (a jump) or progressively (a drift) over time. This evolution is the result of changes in the system due to a leak, damage to equipment or an adjustment, etc. When a static pattern-recognition method is used to construct models of classes for an evolving system, it allows us to classify new observations by comparing them to existing ones. It does not, however, take into account new characteristic information that is used to update models of classes (membership functions). As a result, a static classification system is not very well suited to representing the current characteristics of an evolving system. It is for this reason that this chapter applies the method that we propose for a steam generator in a prototype fast reactor. This method, based on the fuzzy K-nearest neighbors (FKNN) method [KEL 85] is semi-supervised and is called a semi-supervised dynamic fuzzy K-nearest neighbors (SS-DFKNN) method. This allows us to consider new information about an evolving system, detect unknown classes and adapt their characteristics. The SS-DFKNN method was developed to detect and monitor the evolution of dynamic classes ...