Title :
Simulation study on sensor fault diagnoses of the temperature of the boiler high-temperature part metal wall
Author :
Dong, Ze ; Han, Pu ; Yin, Xi-Chao
Author_Institution :
Dept. of Autom., North China Electr. Power Univ., Hebei, China
Abstract :
A sensor fault diagnosis system is designed using SVR. SVR is trained off line, and used online. After being trained, SVR is used to simulate system dynamic characteristic. The simulation result is compared with actual output, and then fault error is drawn. Then use the categorized algorithm of fault based on the SVR of the decision tree, classify the faults. The simulation result shows that, SVR can simulate the system more accurately, thus the fault error is very precise and the classification to fault is correct. This assures the validity of this fault diagnosis system.
Keywords :
boilers; decision trees; fault diagnosis; pattern classification; regression analysis; support vector machines; temperature sensors; walls; boiler wall; decision tree; high temperature metal wall; pattern classification; sensor fault diagnosis system; support vector machine training; support vector regression; temperature sensor; Boilers; Fault diagnosis; Learning systems; Machine learning; Machine learning algorithms; Power generation; Sensor arrays; Support vector machine classification; Support vector machines; Temperature sensors;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1378547
