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Article Dans Une Revue European Safety and RELiability (ESREL) 2010 Conference Année : 2010

Comparison of different methods of aggregation of model ensemble outcomes in the validation and reconstruction of real power plant signals

Résumé

Sensors are placed at various locations in a production plant to monitor the state of the processes and components. For the plant state monitoring to be effective, the sensors themselves must be monitored for detecting anomalies in their functioning and for reconstructing the correct values of the signals measured. In this work, the task of sensor monitoring and signal reconstruction is tackled with an ensemble of Principal Component Analysis (PCA) models handling individual overlapping groups of sensor signals, randomly generated according to the Random Feature Selection Ensemble (RFSE) technique. The outcomes of these models are combined using a Local Fusion (LF) technique based on the evaluation of the models performance on set of training patterns similar to the test pattern under reconstruction. The performances obtained using the LF method are compared to those obtained using classical aggregation methods such as Simple Mean (SM) Globally weighted average (GWA), Median (MD) and Trimmed Mean (TM), on a real case study concerning 215 signals monitored at a Finnish Pressurized Water Reactor (PWR) nuclear power plant.

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Dates et versions

hal-00720991 , version 1 (26-07-2012)

Identifiants

  • HAL Id : hal-00720991 , version 1

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Piero Baraldi, Giulio Gola, Mario Hoffmann, Francesca Mangili, Davide Roverso, et al.. Comparison of different methods of aggregation of model ensemble outcomes in the validation and reconstruction of real power plant signals. European Safety and RELiability (ESREL) 2010 Conference, 2010, pp.1227 - 1232. ⟨hal-00720991⟩
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