Title :
A New Fault Diagnosis Method Based on Immune Model
Author :
Zhou, Gui-hong ; Zuo, Chun-Cheng ; Wang, Jia-zhong ; Liu, Shu-xia
Author_Institution :
Coll. of Inf. Sci. & Technol., Agric. Univ. of Hebei
Abstract :
An immune-based model is proposed to accomplish a non-linear mapping from feature space to a two dimensional classification space in fault diagnosis. The mapping is conducted to create the best cluster effect for training samples belonging to the same class. Especially artificial immune regulation (AIR) scheme used to generate automatically the antibodies (memory B-cells) in the model is explained in detail. Finally the numerical experiments in fault diagnosis of a tapered roller bearing are performed. The results show that the method is simple and effective through comparing with NN
Keywords :
fault diagnosis; learning (artificial intelligence); production engineering; rollers (machinery); rolling bearings; artificial immune regulation; fault diagnosis method; neural networks; nonlinear mapping; tapered roller bearing; Agricultural engineering; Cybernetics; Educational institutions; Electronic mail; Fault detection; Fault diagnosis; Information science; Machine learning; Neural networks; Rolling bearings; Space technology; Immune model; artificial immune regulation; fault diagnosis; tapered roller bearing;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.259145
