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

Abstract

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.