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).

Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues

“…The most evident difference between DETs and static ETs is that while ETs are constructed by expert analysts that draw their branches based on success/failure criteria set by the analysts, in DETs, these are spooned by a software that embeds the (deterministic) models simulating the plant dynamics and the (stochastic) models of components failure. Naturally, the DET generates a number of scenarios much larger than that of the classical static FT/ET approaches, so that the a posteriori retrieval of information can become quite burdensome and complex [125][126][127]. Another challenge is related to the relevant effort in terms of computational time required for generating a large number of time-dependent accident scenarios by means of Monte Carlo techniques that are typically employed to deeply and thoroughly explore the entire system state-space, and to cover in principle all the possible combinations of events over long periods of time.…”

“…Thus, the "a posteriori" retrieval of information can be quite burdensome and difficult. In this view, artificial intelligence techniques could be embraced to address the problem [125][126][127]; iii.…”

Decision Model for Predicting Social Vulnerability Using Artificial Intelligence

“…With this approach, we can find some methodological work [113,114] and others related to biology and medicine, such as a mining study on biological data [115]. Concerning engineering, there is a selection of variables to group road samples [116] and post-processing of accident scenarios [117]. Related to economics and business, there is a discovery of preferences in stock trading [118], focused on this approach of "SOM + decision trees".…”