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

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2954

Lastpage

2958

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.259145

Filename

4028568