Model-based Probabilistic Diagnosis in Large Cyberphysical Systems

Peter J. F. Lucas, Giso H. Dal, A.J. Hommersom, Guus Grievink

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review


Model-based diagnosis is concerned with diagnosing faults or malfunction of real-world physical or cyberphysical systems using a model of the structure and behavior of the systems. As cyberphysical systems can be extremely large and complex, and the associated computational models will be then equally large and complex, they impose a hard to beat challenge on the computational feasibility of reasoning with such models. When such a model is able to handle the uncertainty associated with diagnostics, giving rise to probabilistic model-based diagnostics, the computational feasibility becomes even harder. This paper: (1) proposes a novel graphical method underlying model-based diagnostics; (2) demonstrates experimentally how a novel, by the authors developed architecture of partitioned positive weighted model counting, is able to handle exact inference to answer a variety of probabilistic queries regarding the health status of a cyberphysical system. Results obtained are well within acceptable time bounds.
Original languageEnglish
Title of host publicationProceedings of the European Conference of the PHM Society 2024
EditorsPhuc Do, Cordelia Ezhilarasu
PublisherPHM Society
Number of pages12
ISBN (Print)9781936263400
Publication statusPublished - 27 Jun 2024
Event8th European Conference of the Prognostics and Health Management Society 2024 - Prague, Czech Republic
Duration: 3 Jul 20245 Jul 2024


Conference8th European Conference of the Prognostics and Health Management Society 2024
Abbreviated titlephme24
Country/TerritoryCzech Republic
Internet address


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