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.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62076108, 61972360, and 61872159).
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Huisi Zhou received her MS degree from Jilin University, China in 2020. She is currently a PhD candidate of Jilin University, China. Her research interests include model-based diagnosis and partial maximum satisfiability problem.
Dantong Ouyang received her PhD degree from Jilin University, China in 1998 and she is currently the professor of Jilin University, China. Her research interests include the model-based diagnosis, satisfiability problem and model checking.
Xinliang Tian received his MS degree from Jilin Univercity, China in 2018. He is currently a PhD candidate of Jilin Univercity, China. His main research interests include model-based diagnosis and combinatorial optimization problem.
Liming Zhang received his MS and PhD degree from Jilin Univercity, China in 2009 and 2012, respectively. He is currently a Senior Engineer of Jilin Univercity, China. His main research interests are satisfiability, integrated circuit diagnosis and testing and maximum satisfiability.
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Zhou, H., Ouyang, D., Tian, X. et al. DiagDO: an efficient model based diagnosis approach with multiple observations. Front. Comput. Sci. 17, 176407 (2023). https://doi.org/10.1007/s11704-022-2261-8
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DOI: https://doi.org/10.1007/s11704-022-2261-8