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Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets
This paper presents a model based on Deep Learning algorithms of LSTM and GRU for facilitating an anomaly detection in Large Hadron Collider superconducting magnets. We used high resolution data available in Post Mortem database to train a set of models and chose the best possible set of their hyper...
| Autores principales: | , , |
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| Lenguaje: | eng |
| Publicado: |
2017
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2260346 |