DocumentCode :
3221599
Title :
Application of an information fusion method to compound fault diagnosis of rotating machinery
Author :
Qin Hu ; Aisong Qin ; Qinghua Zhang ; Guoxi Sun ; Longqiu Shao
Author_Institution :
Guangdong provincial Key Lab. of Petrochem. Equip. Fault Diagnosis, Guangdong Univ. of Petrochem. Technol., Maoming, China
fYear :
2015
fDate :
23-25 May 2015
Firstpage :
3859
Lastpage :
3864
Abstract :
Aiming at how to use the multiple fault features information synthetically to improve accuracy of compound fault diagnosis, an information fusion method based on the weighted evidence theory was proposed to effectively diagnose compound faults of rotating machinery. Firstly multiple fault features were extracted by the genetic programming. Each fault feature was separately used to act as evidence and the initial diagnosis accuracy was regarded as the weight coefficient of the evidence. Then through the negative selection algorithm, the diagnosis ability of the local diagnosis was advanced and an impersonal means of obtaining basic probability assignment was given. Finally the fusion result was obtained by utilizing the weighted evidence theory into the decision-making information fusion for the preliminary result. By comparing the diagnosis results with other artificial intelligence algorithm, experiment result indicates that using multiple weighted evidences fusion can improve the diagnostic accuracy of compound fault.
Keywords :
fault diagnosis; feature extraction; genetic algorithms; inference mechanisms; machinery; production engineering computing; sensor fusion; artificial intelligence algorithm; basic probability assignment; compound fault diagnosis; decision-making information fusion; fault feature extraction; genetic programming; information fusion method; multiple fault features information; negative selection algorithm; rotating machinery; weight coefficient; weighted evidence theory; Accuracy; Compounds; Fault diagnosis; Feature extraction; Gears; Shafts; Fusion decision; Genetic programming; Negative selection algorithm; Weighted evidence theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
Type :
conf
DOI :
10.1109/CCDC.2015.7162598
Filename :
7162598
Link To Document :
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