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A critical component of an advanced-gas cooled reactor (AGR) station is the graphite core. As a station ages, the graphite bricks that comprise the core can distort and may eventually crack. As the core cannot be replaced the core integrity ultimately determines the station life. Monitoring these distortions is usually restricted to the routine outages, which occur every few years, as this is the only time that the reactor core can be accessed by external sensing equipment. However, during weekly refueling activities measurements are taken from the core for protection and control purposes. It is shown in this paper that these measurements may be interpreted for condition monitoring purposes, thus potentially providing information relating to core condition on a more frequent basis. This paper describes the data-mining approach adopted to analyze this data and also describes a software system designed and implemented to support this process. The use of this software to develop a model of expected behavior based on historical data, which may highlight events containing unusual features possibly indicative of brick cracking, is also described. Finally, the implementation of this newly acquired understanding in an automated analysis system is described

On-line diagnostics and on-line condition monitoring are important functions within the operation, control and management of power systems. Extensive research activities have led to the development of intelligent system techniques that support these functions. However, experience has shown that often more than one intelligent system technique is required to perform the diagnostic or monitoring function. In addition, integration is required between legacy data sources, legacy monitoring systems and the new data capture systems and intelligent systems being applied. Multi-agent system techniques can be used to provide such integration, while enhancing the overall intelligent interpretation functionality

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