基于动态聚类的有限状态机多错误诊断  

Multiple-fault Diagnosis of Finite State Machine Based on Dynamic Clustering

作　　者：崔玲 张建标 CUI Ling;ZHANG Jianbiao(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Key Laboratory of Trusted Computing,Beijing 100124,China;National Engineering Laboratory for Critical Technologies of Information Security Classified Protection,Beijing 100124,China)

机构地区：[1]北京工业大学信息学部,北京100124 [2]可信计算北京市重点实验室,北京100124 [3]信息安全等级保护关键技术国家工程实验室,北京100124

出　　处：《北京工业大学学报》2021年第6期607-615,共9页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(61501007)。

摘　　要：为了解决多错误诊断时枚举数量过大的问题,提出一种基于动态聚类分析的方法.首先,按照是否具有相同的初始症状冲突集对失败用例进行聚类,并计算初始症状冲突集及其转换的可疑度;然后,按照可疑度的大小枚举可能发生错误的转换组合,在枚举过程中进行再次聚类;最后,用测试集验证错误可能,生成错误诊断集.实验结果表明,该方法可以有效减少错误枚举数量,提高诊断效率.To solve the problem of too many enumerations in multiple-fault diagnosis,a method based on dynamic clustering analysis was proposed.First,the failed test cases were clustered according to whether they have the same conflict set of initial symptom,and the suspicious degree of the conflict set of initial symptom and its transitions was calculated with this method.Then,the possible fault combinations of transitions were enumerated according to the suspicious degrees.In this process,the failed test cases needed clustering again.Finally,the test set was used to verify the possibility of faults and to generate the fault diagnosis set.Results show that this method can effectively reduce the number of faults enumeration and improve the diagnosis efficiency.

关 键 词：多错误诊断 动态聚类 初始症状 冲突集 错误诊断集 诊断效率 

分 类 号：TP319[自动化与计算机技术—计算机软件与理论]