Unsupervised Clustering for Fault Diagnosis in Nuclear Power Plant Components
The development of empirical classification models for fault diagnosis usually requires a process of training based on a set of examples. In practice, data collected during plant operation contain signals measured in faulty conditions, but they are ‘unlabeled’, i.e., the indicati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2013-08-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25868421.pdf |