基于多特征云关联度的容差电路诊断算法  

Fault diagnosis for circuits with tolerance based on multi-feature cloud relational matrix

作　　者：魏子杰[1] 陈圣俭[1] WEI Zijie;CHEN Shengian(Deparimeni of Control Engineering , Academy of Armored Force Engineering , Beijing 100072 , China)

机构地区：[1]装甲兵工程学院控制工程系,北京100072

出　　处：《系统工程与电子技术》2017年第12期2665-2670,共6页Systems Engineering and Electronics

基　　金：国家自然科学基金(61179001)资助课题

摘　　要：针对容差模拟电路故障诊断中存在的模糊性和随机性问题,提出了一种基于多特征云关联度的诊断算法。首先,结合模拟电路自身特性,提出与之相适合的组合云模型。之后,通过构建云关联矩阵,对不同电路特征的云隶属度进行综合评价。考虑到不同特征对总体云关联度的贡献不同,根据特征类内和类间散度对云关联度矩阵进行加权。最后,通过求取待识别特征向量与各故障模式的云关联序列,实现对电路故障的识别诊断。实例分析表明,所提方法在容差条件下对电路软故障有较高的诊断效率。To solve the problem of fuzziness and randomness in fault diagnosis of tolerance analog circuits,a diagnosis method based on the multi-feature cloud correlation matrix is proposed.First,a combined cloud model is proposed according to the characteristics of analog circuits.Then,by constructing the cloud correlation matrix,the cloud membership degree of different circuit features is evaluated synthetically.Considering the different contributions of different features to the overall cloud correlation,the cloud correlation matrix is weighted according to the within-class and between-class scatter.Finally,the identification of the fault is realized by obtaining the cloud-related sequences of the feature vectors to be identified and the failure modes.The example analysis shows that the proposed method has higher diagnostic efficiency for circuit soft faults under the tolerance condition.

关 键 词：模拟电路 组合云模型 特征权重向量 云关联矩阵 故障诊断 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]