Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) of dynamic systems is challenged by the need of implementing efficient methods for accidental scenarios generation (that are to be increased with respect to conventional PSA, due to the necessary consideration of failure events timing and sequencing along the scenarios) and for their post-processing for retrieving safety relevant information regarding the system behavior (that, in the context of IDPSA consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs) and Prime Implicants (PIs)). The large amount of generated scenarios makes the computational cost for scenario post-processing enormous and the retrieved information difficult to interpret. To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self Organizing Maps (SSSOM) whose outcomes are combined by a locally weighted aggregation: we resort to the Local Fusion (LF) principle for accounting the classification reliability of the different SSSOM classifiers, for the type of scenario to be classified. The strategy is applied for the post-processing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG).

Local fusion of an ensemble of semi-supervised self organizing maps for post-processing accidental scenarios

Di Maio, Francesco;Zio, Enrico
2017-01-01

Abstract

Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) of dynamic systems is challenged by the need of implementing efficient methods for accidental scenarios generation (that are to be increased with respect to conventional PSA, due to the necessary consideration of failure events timing and sequencing along the scenarios) and for their post-processing for retrieving safety relevant information regarding the system behavior (that, in the context of IDPSA consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs) and Prime Implicants (PIs)). The large amount of generated scenarios makes the computational cost for scenario post-processing enormous and the retrieved information difficult to interpret. To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self Organizing Maps (SSSOM) whose outcomes are combined by a locally weighted aggregation: we resort to the Local Fusion (LF) principle for accounting the classification reliability of the different SSSOM classifiers, for the type of scenario to be classified. The strategy is applied for the post-processing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG).
International Topical Meeting on Probabilistic Safety Assessment and Analysis, PSA 2017
9781510851801
Ensemble of classifiers; Integrated deterministic; Local fusion; Probabilistic safety analysis; Scenarios post-processing; Self-organizing map; Safety, Risk, Reliability and Quality; Statistics, Probability and Uncertainty; Statistics and Probability; Nuclear Energy and Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1055767
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