Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps

Abstract

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).