scholarly journals A novel plugged tube detection and identification approach for final super heater in thermal power plant using principal component analysis

Energy ◽  
2017 ◽  
Vol 126 ◽  
pp. 404-418 ◽  
Author(s):  
Jungwon Yu ◽  
Jaeyeong Yoo ◽  
Jaeyel Jang ◽  
June Ho Park ◽  
Sungshin Kim
Keyword(s):  
Principal Component Analysis ◽  
Power Plant ◽  
Thermal Power Plant ◽  
Thermal Power ◽  
Principal Component ◽  
Component Analysis ◽  
Detection And Identification ◽  
Super Heater ◽  
Identification Approach

Failure Investigation of Radiant Platen Superheater Tube of Thermal Power Plant Boiler

2014 ◽  
Vol 0 (0) ◽  
Author(s):  
D. Ghosh ◽  
S. Ray ◽  
A. Mandal ◽  
H. Roy
Keyword(s):  
Power Plant ◽  
Thermal Power Plant ◽  
Thermal Power ◽  
Metallographic Analysis ◽  
Premature Failure ◽  
Superheater Tube ◽  
Power Plant Boiler ◽  
Super Heater ◽  
Cause Of Failure ◽  
Plant Boiler

AbstractThis paper highlights a case study of typical premature failure of a radiant platen superheater tube of 210 MW thermal power plant boiler. Visual examination, dimensional measurement and chemical analysis, are conducted as part of the investigations. Apart from these, metallographic analysis and fractography are also conducted to ascertain the probable cause of failure. Finally it has been concluded that the premature failure of the super heater tube can be attributed to localized creep at high temperature. The corrective actions has also been suggested to avoid this type of failure in near future.

Fault Detection and Identification in NPP Instruments Using Kernel Principal Component Analysis

2010 ◽  
Author(s):  
Jianping Ma ◽  
Jin Jiang
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Power Plants ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Isolation ◽  
Kernel Principal Component Analysis ◽  
Isolation And Identification ◽  
Detection And Identification ◽  
Fault Detection And Identification

In this paper, kernel principal component analysis (KPCA) is studied for fault detection and identification in the instruments of nuclear power plants. We propose to use mean values of the sensor reconstruction errors of a KPCA model for fault isolation and identification. They provide useful information about the directions and magnitudes of detected faults, which are usually not available from other fault isolation techniques. The performance of the method is demonstrated by applications to real NPP measurements.

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