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Model-based diagnosis with low-cost fault identification

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Conclusions

In this paper, we propose an efficient algorithm for solving the posterior failure probabilities of components, which can approximate a ranking of failure probabilities for all components. When solving large-scale circuits, the time required to solve MHSs significantly affects the overall efficiency of the algorithm, making it difficult for many algorithms to return the posterior failure probabilities of components. Initially, we utilize the BAMHS algorithm combined with an incremental strategy to propose a solving framework. Subsequently, we present two important propositions for the elimination of redundant hitting sets. Finally, we provide the specific expressions for the minimization parameters.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61876071, and 62076108), and the Scientific and Technological Developing Scheme of Jilin Province (20180201003SF, 20190701031GH).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Jihong Ouyang, Sen Huang & Liming Zhang

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun, 130012, China

    Jihong Ouyang, Sen Huang & Liming Zhang

  3. School of Computer and Control Engineering, Yantai University, Yantai, 264005, China

    Xiangfu Zhao

Authors
  1. Jihong Ouyang
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  2. Sen Huang
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  3. Liming Zhang
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  4. Xiangfu Zhao
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Corresponding author

Correspondence to Sen Huang.

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Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.

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Ouyang, J., Huang, S., Zhang, L. et al. Model-based diagnosis with low-cost fault identification. Front. Comput. Sci. 19, 195333 (2025). https://doi.org/10.1007/s11704-024-40393-y

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  • DOI: https://doi.org/10.1007/s11704-024-40393-y