| contributor author | Jianping Ma | |
| contributor author | Jin Jiang | |
| date accessioned | 2017-05-09T00:50:32Z | |
| date available | 2017-05-09T00:50:32Z | |
| date copyright | March, 2012 | |
| date issued | 2012 | |
| identifier issn | 1528-8919 | |
| identifier other | JETPEZ-27186#032901_1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/148907 | |
| description abstract | 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. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Detection and Identification of Faults in NPP Instruments Using Kernel Principal Component Analysis | |
| type | Journal Paper | |
| journal volume | 134 | |
| journal issue | 3 | |
| journal title | Journal of Engineering for Gas Turbines and Power | |
| identifier doi | 10.1115/1.4004596 | |
| journal fristpage | 32901 | |
| identifier eissn | 0742-4795 | |
| keywords | Measurement | |
| keywords | Sensors | |
| keywords | Instrumentation | |
| keywords | Errors | |
| keywords | Flaw detection | |
| keywords | Principal component analysis AND Nuclear power stations | |
| tree | Journal of Engineering for Gas Turbines and Power:;2012:;volume( 134 ):;issue: 003 | |
| contenttype | Fulltext | |