Research of Anomaly Detection Method Based on Improved Artificial Immunity
According to the abnormal state detection without enough priori knowledge and fault samples, a new detector generation method with step radiuses is proposed. The method separates abnormal state space into several regions, and then sets different detector radius for them according to actual situations, has great flexibility and higher coverage rate to space. Artificial immune real-valued negative selection algorithm based on this method is applied to found the model for abnormal state detection, and then we test and analyze two different cases to some bearing using the model founded above: one with the fault samples and the other not. A conclusion is reached from the test that the method mentioned above can detect the known and unknown fault efficiently, and the correct detection rate is satisfactory.
artificial immunity novelty detection fault diagnosis step radius negative selection
Xinpeng ZHANG Niaoqing HU Lei HU
Laboratory of Science and Technology on Integrated Logistics Support National University of Defense Technology Changsha, China
国际会议
西安
英文
1057-1061
2011-06-17（万方平台首次上网日期，不代表论文的发表时间）