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ECOLE SUPÉRIEURE D'ÉLECTRICITÉ Une grande école d'ingénieurs au cœur des sciences de l'information,de l'énergie et des systèmes |
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Journal articles
Two novel procedures for aggregating randomized model ensemble outcomes for robust signal reconstruction in nuclear power plants monitoring systems
Abstract : Detecting anomalies in sensors and reconstructing the correct values of the measured signals is of paramount importance for the safe and reliable operation of nuclear power plants. Auto-associative regression models can be used for the signal reconstruction task but in real applications the number of sensors signals may be too large to be handled effectively by one single model. In these cases, one may resort to an ensemble of reconstruction models, each one handling a small group of sensor signals; the outcomes of the individual models are then combined to produce the final reconstruction. In this work, three methods for aggregating the outcomes of a feature-randomized ensemble of Principal Components Analysis (PCA)-based regression models are analyzed and applied to two case studies concerning the reconstruction of 215 signals monitored at a Finnish nuclear Pressurized Water Reactor (PWR) and 920 simulated signals of the Swedish Forsmark-3 Boiling Water Reactor (BWR). Based on the insights gained, two novel aggregation procedures are developed for optimal signal reconstruction.
Cited literature [18 references]
https://hal-supelec.archives-ouvertes.fr/hal-00609545
Contributor : Yanfu Li <>
Submitted on : Friday, July 27, 2012 - 2:34:43 PM
Last modification on : Monday, December 14, 2020 - 12:28:23 PM
Long-term archiving on: : Sunday, October 28, 2012 - 2:35:36 AM
Contributor : Yanfu Li <>
Submitted on : Friday, July 27, 2012 - 2:34:43 PM
Last modification on : Monday, December 14, 2020 - 12:28:23 PM
Long-term archiving on: : Sunday, October 28, 2012 - 2:35:36 AM
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