摘要
模型诊断方法是人工智能领域重要的系统故障自动检测方法,被广泛应用于软件故障检测和硬件诊断.近年来由于电路规模和复杂度不断增大,其诊断难度也不断增大.本文通过对电路模型特征的研究,结合LLBRStree(Last-Level Based on Reverse Search-tree)诊断算法提出分组式诊断方法 GD(Grouped Diagnosis):首先结合电路特征确定组件的故障相关性并对电路组件进行分组,可缩减电路中需检测的规模;其次,利用分组后电路并结合非诊断解定理和SAT(SATisfiability)求解特征定位部分非诊断解,从而避免该部分的一致性检测来加速求解.本文算法可应用于电子电路故障诊断领域,并且实验结果表明该算法与LLBRS-tree算法相比求解效率平均提高了1.5倍,最多提高了3倍.
Model-based diagnosis is an automatic fault detection approach in artificial intelligence.It is used in software fault detection and hardware diagnosis.Recently,the difficulty of circuit diagnosis is increasing with the increasing size and complexity of the circuit.After studying the characteristics of the circuit model,this paper proposes the grouped diagnosis(GD)approach based on the LLBRS-tree(Last-Level Based on Reverse Search-tree)algorithm.Firstly,the component grouped method is used to identify the component's faulty correlation and group the components.And the scale of the circuit to be detected can be reduced.Secondly,through the grouped circuit,the non-diagnostic solution theorem is given to locate some non-diagnostic solutions with the feature of satisfiability.It can help us avoid checking consistency on these non-diagnostic solutions so as to accelerate the processing.Our algorithm can be used in electronic circuit fault diagnosis.And the experimental results show that it improves the efficiency by 1.5 times and even 3 times compared with the LLBRS-tree algorithm.
作者
刘梦
欧阳丹彤
刘伯文
张立明
张永刚
LIU Meng;OUYANG Dan-tong;LIU Bo-wen;ZHANG Li-ming;ZHANG Yong-gang(College of Computer Science and Technology,Jilin University,Changchun,Jilin130012,China;Key Laboratory of Symbolic Computation and Knowledge Engineering(Jilin University),Ministry of Education,Changchun,Jilin130012,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2018年第3期589-594,共6页
Acta Electronica Sinica
基金
国家自然科学基金(No.61272208
No.61402196
No.61672261)
浙江省自然科学基金(No.LY16F020004)
