Title :
Fault Diagnosis for Gas Turbine Blade Based on ABC-RVM Algorithm
Author :
Chen Li-Wei ; Pu Ying-Dong
Author_Institution :
Coll. of Inf. & Commun. Eng., Harbin Eng. Univ., Harbin, China
Abstract :
In this paper, a fault diagnosis scheme for gas turbine blade is developed. The proposed system is based on the artificial bee colony algorithm optimize relevance vector machine (ABC-RVM) to accomplish this goal. First the characteristics extraction was researched, then Then ABC-RVM is used for the intelligent fault diagnosis and health warning provides scientific theory and effective method for the fault diagnosis.
Keywords :
blades; fault diagnosis; gas turbines; learning (artificial intelligence); mechanical engineering computing; optimisation; ABC-RVM algorithm; artificial bee colony algorithm; gas turbine blade; health warning; intelligent fault diagnosis; relevance vector machine; Blades; Fault diagnosis; Sociology; Statistics; Support vector machines; Training; Turbines; ABC-RVM; fault diagnosis; temperature signal;
Conference_Titel :
Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2014 Fourth International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4799-6574-8
DOI :
10.1109/IMCCC.2014.27
