Abstract
A new modular multi-microprocessor system for on-line vibration monitoring and diagnostics of PWRs is described. The aim of the system is to make feasible an early detection of increasing failures in relevant regions of a reactor plant, to verify the mechanical integrity of the investigated components, and to improve therefore the operational safety of the plant. After a discussion of the implemented surveillance methods and algorithms, which are based on hierarchical structured identification (estimation) and statistical pattern recognition tools, the system architecture (software and hardware) is portrayed. The classification scheme itself works sequential so that samples (or features) can arrive on-line. This on-line classification is important in order to take necessary actions in time. Furthermore, the system has learning capabilities, which means it is adaptable to different, varying states and plant conditions. The main features of the system are presented and its contribution to an automation of complex surveillance and monitoring tasks is shown.
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