基于CBR的无线闭塞中心故障诊断方法研究

无线闭塞中心(RBC)系统结构、功能复杂,维护过程中积累了大量的现场诊断案例。为了有效利用历史诊断经验,将人工智能CBR(Case-Based Reasoning)技术引入到无线闭塞中心的故障诊断中,分析基于CBR的RBC故障诊断流程,运用面向对象的方法对RBC故障案例进行了表示,提出基于R-S(Rough Set)理论的案例特征属性权重计算方法,采用融合最近邻和余弦函数的相似度算法改进了传统案例推理技术的相似度算法。最后以RBC维护终端的具体案例验证提出方法的有效性。

Fault Diagnosis Method of Radio Block Center Based on Case-Based Reasoning

Due to the complexity of system structure and functionalities of Radio Block Center(RBC), a large number of on-site diagnostic cases are accumulated during maintenance. In order to make full use of historical diagnostic experiences, this paper introduces Case-Based Reasoning(CBR) technology of artificial intelligence into the fault diagnosis of RBC, and analyzes the flow of RBC fault diagnosis based on CBR. The Object-Oriented Method is used to represent RBC fault cases and the weight calculation method of cases' feature attribute based on Rough Set Theory is proposed. Further, the similarity algorithm by blending the nearest neighbor and the cosine function is employed to improve the similarity algorithm of traditional CBR technology. Finally, the effectiveness of the proposed method is verified by concrete failure cases of RBC maintenance terminal.