DiagDO: an efficient model based diagnosis approach with multiple observations

Abstract

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.

Graphical abstract

Keywords

model based diagnosis / maximum satisfiability / top-level diagnosis / cardinality-minimal diagnosis / subset-minimal diagnosis

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