Name
Abstract | ||
|---|---|---|
We propose a visual interactive method for the identification of the Prime Implicants (PIs) of dynamic non-coherent systems. Visual interactive methods integrate mathematical and symbolic models with runtime interaction and real-time graphic display, which allow visualizing the underlying physical relationships among process parameters. The proposed method is based on a parallel coordinates data mining tool that relies on an innovative pruning procedure which, on the basis of a proper selection of characteristic features of the accident sequences, retrieves the PIs among the whole set of implicants in terms of process parameter values or component failure states or both. The method is exemplified on an artificial case study, and then applied to the dynamic reliability analysis of the Airlock System of a Canadian Deuterium Uranium reactor. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TR.2014.2371015 | Reliability, IEEE Transactions |
Keywords | Field | DocType |
dynamic reliability,data mining,nuclear power plants,paraller coordinates diagram,prime implicants,inductors,uranium,visualization,mathematical models,reliability,deuterium,data visualization,feature selection | Data mining,Data visualization,Computer science,Visualization,Process variable,Dynamic reliability,Parallel coordinates,Implicant,Statistics,Reliability engineering | Journal |
Volume | Issue | ISSN |
64 | 2 | 0018-9529 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Di Maio | 1 | 13 | 1.70 |
| Samuele Baronchelli | 2 | 0 | 0.34 |
| Enrico Zio | 3 | 1100 | 145.38 |