| Titre : | Detection and identification of faults in NPP instruments using kernel principal component analysis (2012) |
| Auteurs : | Jianping Ma, Auteur ; Jin Jiang, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Transactions of the ASME . Journal of engineering for gas turbines and power (Vol. 134 N° 3, Mars 2012) |
| Article en page(s) : | 06 p. |
| Note générale : | Génie mécanique |
| Langues : | Anglais |
| Index. décimale : | 620.1 (Essais des matériaux. Défauts des matériaux. Protection des matériaux) |
| Tags : | Fault diagnosis Instruments Nuclear power stations Power generation faults system identification Principal component analysis Sensor fusion |
| Résumé : | In this paper, kernel principal component analysis (KPCA) is studied for fault detection and identification of the instruments in nuclear power plants. A KPCA model for fault isolation and identification is proposed by using the average sensor reconstruction errors. Based on this model, faults in multiple sensors can be isolated and identified simultaneously. Performance of the KPCA-based method is demonstrated with real NPP measurements. |
| DEWEY : | 620.1 |
| ISSN : | 0742-4795 |
| En ligne : | http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000134000003032901000001&idtype=cvips&gifs=Yes&ref=no |
