Large-scale signal reconstruction for sensor monitoring and diagnostics in nuclear power plants
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Abstract
On-line sensor monitoring and diagnostics systems aim at detecting anomalies in sensors and reconstructing their correct signals during operation. Since 1994, research at the OECD Halden Reactor Project has focused on the problem of sensor monitoring and diagnostics, eventually leading to the development of the PEANO system for signal validation and reconstruction. PEANO combines empirical techniques like Fuzzy Clustering and Auto- Associative Neural Networks and has proved to be successful in a variety of practical applications. Nevertheless, using one single empirical model sets a limit to the number of signals that can be handled at a time. Recently, efforts have been made to extend the applicability of PEANO to the whole plant, which requires the validation and reconstruction of thousands of signals. This has entailed moving from a single-model to an ensemble-of-model approach which has involved the investigation of new issues. This paper presents the method hereby developed for on-line, large scale sensor monitoring and signal reconstruction and a practical application of the method to the reconstruction of signals measured at nuclear power plants.
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ensemble methods, applications: nuclear, sensor monitoring, signal validation, empirical models
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