Large scale nuclear sensor monitoring and diagnostics by means of an ensemble of regression models based on Evolving Clustering Methods
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| Titel: | Large scale nuclear sensor monitoring and diagnostics by means of an ensemble of regression models based on Evolving Clustering Methods |
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| Autoren: | G. Gola, D. Roverso, M. Hoffmann, BARALDI, PIERO, ZIO, ENRICO |
| Weitere Verfasser: | Li, Yanfu, Institute for Energy Technology, OECD Halden Reactor Project, Dipartimento di Energia Milano, Politecnico di Milano Milan (POLIMI), Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec (SSEC), Ecole Centrale Paris-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-CentraleSupélec-EDF R&D (EDF R&D), EDF (EDF)-EDF (EDF), Laboratoire Génie Industriel - EA 2606 (LGI), CentraleSupélec |
| Verlagsinformationen: | HAL CCSD, 2010. |
| Publikationsjahr: | 2010 |
| Schlagwörter: | [SPI.OTHER]Engineering Sciences [physics]/Other, Evolving Clustering Methods, [SPI.OTHER] Engineering Sciences [physics]/Other, Sensor monitoring, Ensemble, Signal reconstruction |
| Beschreibung: | On-line sensor monitoring systems aim at detecting anomalies in sensors and reconstructing their correct signals during operation. Auto-associative regression models are usually adopted to perform the signal reconstruction task. In full scale implementations however, the number of sensors to be monitored is very large and cannot be handled effectively by a single reconstruction model. This paper tackles this issue by resorting to an ensemble of reconstruction models in which each model handles a small group of signals. In this view, firstly a procedure for generating the signal groups must be set. Then, a corresponding number of signal reconstruction models must be built on the bases of the groups and, finally, the outcomes of the reconstruction models must be aggregated. In this paper, three different signal grouping approaches are devised for comparison: pure-random, random-filter and random-wrapper. Signals are then reconstructed by Evolving Clustering Method (ECM) models. The median of the outcomes distribution is here retained as the ensemble aggregate. The ensemble approach is applied to a real case study concerning the validation and reconstruction of 792 signals measured at the Swedish boiling water reactor located in Oskarshamn. |
| Publikationsart: | Article Conference object |
| Dateibeschreibung: | application/pdf |
| Sprache: | English |
| Zugangs-URL: | https://hal.science/hal-00720974v1 https://hdl.handle.net/11311/581712 |
| Dokumentencode: | edsair.dedup.wf.002..c5a0600ab6853a327671e7e98f183ffc |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | On-line sensor monitoring systems aim at detecting anomalies in sensors and reconstructing their correct signals during operation. Auto-associative regression models are usually adopted to perform the signal reconstruction task. In full scale implementations however, the number of sensors to be monitored is very large and cannot be handled effectively by a single reconstruction model. This paper tackles this issue by resorting to an ensemble of reconstruction models in which each model handles a small group of signals. In this view, firstly a procedure for generating the signal groups must be set. Then, a corresponding number of signal reconstruction models must be built on the bases of the groups and, finally, the outcomes of the reconstruction models must be aggregated. In this paper, three different signal grouping approaches are devised for comparison: pure-random, random-filter and random-wrapper. Signals are then reconstructed by Evolving Clustering Method (ECM) models. The median of the outcomes distribution is here retained as the ensemble aggregate. The ensemble approach is applied to a real case study concerning the validation and reconstruction of 792 signals measured at the Swedish boiling water reactor located in Oskarshamn. |
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