Super Base Station Fault Detection Mechanism Based on Negative Selection Algorithm and Expert Knowledge Base

2nd International Symposium on Application of Materials Science and Energy Materials

材料科学;能源学

Ye, Guanwen^1^2 ; Wang, Yuanyuan^2 ; Sun, Qian^2

Chongqing University of Posts and Telecommunications, Chongqing, China^1

Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China^2

关键词: Artificial immunity;    Business data;    Expert knowledge-base;    Explosive growth;    Fault management;    Fault management system;    Fault-detection mechanisms;    Negative selection algorithm;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/490/7/072019/pdf
DOI  :  10.1088/1757-899X/490/7/072019

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

With the expansion of network scale and the increase of equipment complexity, the super base station business data has experienced explosive growth, which makes fault detection more and more difficult, and the efficiency of fault management is getting lower and lower. To solve the above problems, this paper designs a comprehensive fault detection mechanism (NSAEFD) by combining the negative selection algorithm in the field of artificial immunity and expert system. NSAEFD is further introduced in detail in super base station(SBS). NSAEFD is also easy to implement and can be well applied to the fault management system of the SBS.