Name
Title | ||
|---|---|---|
A framework for reconciliating data clusters from a fleet of nuclear power plants turbines for fault diagnosis. |
Abstract | ||
|---|---|---|
Framework for reconciliating the consensus clusterings for a fleet of P components. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.asoc.2018.04.044 | Applied Soft Computing |
Keywords | Field | DocType |
Fault diagnosis,Unsupervised ensemble clustering,Incremental learning,Cluster reconciliation,Fleet of nuclear power plants (NPPs) turbines shut-down | Data mining,Cluster (physics),Consensus clustering,Artificial intelligence,Machine learning,Nuclear power,Mathematics,Anomalous behavior | Journal |
Volume | ISSN | Citations |
69 | 1568-4946 | 1 |
PageRank | References | Authors |
0.40 | 25 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sameer Al-Dahidi | 1 | 20 | 2.67 |
| Francesco Di Maio | 2 | 124 | 14.20 |
| Piero Baraldi | 3 | 236 | 21.96 |
| Enrico Zio | 4 | 77 | 7.43 |
| Redouane Seraoui | 5 | 41 | 2.86 |