Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps
Joint Authors
Rossetti, Roberta
Di Maio, Francesco
Zio, Enrico
Source
Science and Technology of Nuclear Installations
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-12
Country of Publication
Egypt
No. of Pages
14
Abstract EN
Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) of dynamic systems calls for the development of efficient methods for accidental scenarios generation.
The necessary consideration of failure events timing and sequencing along the scenarios requires the number of scenarios to be generated to increase with respect to conventional PSA.
Consequently, their postprocessing for retrieving safety relevant information regarding the system behavior is challenged because of the large amount of generated scenarios that makes the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret.
In the context of IDPSA, the interpretation consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs), and Prime Implicants (PIs).
To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self-Organizing Maps (SSSOMs) whose outcomes are combined by a locally weighted aggregation according to two strategies: a locally weighted aggregation and a decision tree based aggregation.
In the former, we resort to the Local Fusion (LF) principle for accounting the classification reliability of the different SSSOM classifiers, whereas in the latter we build a classification scheme to select the appropriate classifier (or ensemble of classifiers), for the type of scenario to be classified.
The two strategies are applied for the postprocessing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG).
American Psychological Association (APA)
Di Maio, Francesco& Rossetti, Roberta& Zio, Enrico. 2017. Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps. Science and Technology of Nuclear Installations،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1203618
Modern Language Association (MLA)
Di Maio, Francesco…[et al.]. Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps. Science and Technology of Nuclear Installations No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1203618
American Medical Association (AMA)
Di Maio, Francesco& Rossetti, Roberta& Zio, Enrico. Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps. Science and Technology of Nuclear Installations. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1203618
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1203618