A model-based ensemble approach to plant-wide online sensor monitoring. (1st January 2011)

• Record Type: Journal Article
• Authors: Gola, Giulio; Roverso, Davide; Hoffmann, Mario
• Is Part Of: International journal of nuclear knowledge management. Volume 5:Number 2(2011)
• Page Start: 148
• Page End: 161
• Publication Date: 2011-01-01
• Subjects: sensor monitoring -- signal reconstruction -- nuclear application -- ensemble -- evolving clustering method; Nuclear energy -- Periodicals; Nuclear energy -- Management -- Periodicals; Nuclear industry -- Periodicals; Nuclear industry -- Management -- Periodicals; 333.792405
• DOI: 10.1504/IJNKM.2011.040939
• Languages: English
• ISSNs: 1479-540X

Abstract

Online sensor monitoring aims 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, eventually developing the PEANO system for signal validation. PEANO combines fuzzy clustering and auto-associative neural networks and has proved 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, PEANO has been extended to cover the validation of all the plant signals. This has entailed shifting from a single-model to a model-ensemble approach. This paper illustrates the plant-wide extension of the PEANO system and its practical application to a real case study.