DocumentCode :
3370144
Title :
Study on fault diagnosis algorithm based on artificiall immune danger theory
Author :
Meng, Qinghua ; Zhao, Wenli
Author_Institution :
Sch. of Mech. Eng., Hangzhou Danzi Univ., Hangzhou, China
fYear :
2010
fDate :
26-28 June 2010
Firstpage :
5997
Lastpage :
6000
Abstract :
In order to improve precision of fault diagnosis which based on artificial immune system, a kind of fault diagnosis algorithm based on immune danger theory was presented. The algorithm can make judgment according to whether existing danger signals and reduce false rate. The algorithm also can adjust databases online. The algorithm was applied to automobile axle driving fault diagnosis. the result shows that 6% normal axle drivings are judged as abnormal axle drivings, 4% abnormal axle drivings are judged as normal axle drivings. Compared with testing result of advanced negative selection algorithm which based on self-nonself recognition, the fault diagnosis algorithm based on artificial immune danger theory result has a lower false rate.
Keywords :
artificial immune systems; automotive engineering; axles; fault diagnosis; mechanical engineering computing; artificial immune danger theory; automobile axle driving fault diagnosis; fault diagnosis algorithm; self-nonself recognition; Artificial immune systems; Automatic testing; Automobiles; Automotive engineering; Axles; DNA; Databases; Fault diagnosis; Immune system; Mechanical engineering; Danger theory; Fault diagnosis; Immune system; automobile axle driving;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mechanic Automation and Control Engineering (MACE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-7737-1
Type :
conf
DOI :
10.1109/MACE.2010.5536845
Filename :
5536845
Link To Document :
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