By Topic

Fault Detection Based on Real-Value Negative Selection Algorithm of Artificial Immune System

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Xin Yue ; Coll. of Comput. & Inf. Eng., Heilongjiang Inst. of Sci. & Technol., Harbin, China ; Dongge Wen ; Haifeng Ma ; Jianfei Zhang

Applications of Artificial Immune System (AIS) has been widely applied in various engineering, including network security, pattern recognition, combinational optimization, machine learning and fault diagnosis, etc. Fault diagnosis is another AIS application field directly mapped from the theory of immunity after information security and has made certain achievements in research. Real-valued Negative selection algorithms (RNSA) of AIS generate their detector sets based on the points of self data. Self data is regarded as the normal pattern of behavior of the monitored system. This paper provide a new fault detection method based on RNSA of artificial immunity. It can effectively overcome the deficiency of the various fault detection methods of today that cannot implement fault detections because there are only normal samples and not enough fault samples and short of the function of continuous learning. The test result shows that, by increasing a certain number of training samples, the accuracy of fault diagnosis has made great changes. This way has obvious advantage in robustness and accuracy in detection and shows a favorable prospect of application.

Published in:

Intelligent Computing and Cognitive Informatics (ICICCI), 2010 International Conference on

Date of Conference:

22-23 June 2010