DiagDO: an efficient model based diagnosis approach with multiple observations  

作　　者：Huisi ZHOU Dantong OUYANG Xinliang TIAN Liming ZHANG 

机构地区：[1]Laboratory of Symbolic Computation and Knowledge Engineering,Jilin University,Changchun 130000,China [2]College of Computer Science and Technology,Jilin University,Changchun 130000,China

出　　处：《Frontiers of Computer Science》2023年第6期125-134,共10页中国计算机科学前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.62076108,61972360,and 61872159).

摘　　要：Model-based diagnosis(MBD)with multiple observations shows its significance in identifying fault location.The existing approaches for MBD with multiple observations use observations which is inconsistent with the prediction of the system.In this paper,we proposed a novel diagnosis approach,namely,the Diagnosis with Different Observations(DiagDO),to exploit the diagnosis when given a set of pseudo normal observations and a set of abnormal observations.Three ideas are proposed in this paper.First,for each pseudo normal observation,we propagate the value of system inputs and gain fanin-free edges to shrink the size of possible faulty components.Second,for each abnormal observation,we utilize filtered nodes to seek surely normal components.Finally,we encode all the surely normal components and parts of dominated components into hard clauses and compute diagnosis using the MaxSAT solver and MCS algorithm.Extensive tests on the ISCAS'85 and ITC'99 benchmarks show that our approach performs better than the state-of-the-art algorithms.

关 键 词：model based diagnosis maximum satisfiability top-level diagnosis cardinality-minimal diagnosis subset-minimal diagnosis 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]