Keywords: nuclear signal reconstruction, principal component analysis, PCA, ensemble aggregation, sensor monitoring, reconstruction models, nuclear reactors, boiling water reactors, BWR, pressurised water reactors, PWR, nuclear energy, nuclear power
Robust nuclear signal reconstruction by a novel ensemble model aggregation procedure
Monitoring of sensor operation is important for detecting anomalies and reconstructing the correct values of the signals measured. This can be done, for example, with the aid of auto-associative regression models. However, in practical applications, difficulties arise because of the need for handling large numbers of signals. To overcome these difficulties, ensembles of reconstruction models can be used. Each model in the ensemble handles a small group of signals and the outcomes of all models are eventually combined to provide the final outcome. In this work, three different methods for aggregating the model outcomes are investigated and a novel procedure is proposed for obtaining robust ensemble-aggregated outputs. Two applications are considered concerning the reconstruction of 920 simulated signals of the Swedish Forsmark-3 Boiling Water Reactor (BWR) and 215 signals measured at the Finnish Pressurised Water Reactor (PWR) situated in Loviisa.