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Beyond Dynamic Bayesian Networks: Fusing Temporal Logic Monitors with Probabilistic Diagnosis (Short Paper)

Authors Chetan Kulkarni, Johann Schumann

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OASIcs.DX.2025.13.pdf
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Subject Classification

ACM Subject Classification
  • Computing methodologies → Causal reasoning and diagnostics
  • Computing methodologies → Temporal reasoning
  • Mathematics of computing → Probabilistic inference problems
  • Computing methodologies → Multiscale systems
  • Mathematics of computing → Bayesian networks
Keywords
  • Bayesian diagnostic network
  • temporal logic
  • fault diagnosis
  • temporal reasoning
  • probabilistic inference
  • scalability

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Abstract

Conventional diagnostic systems often fail to account for temporal dynamics - such as duration, frequency, or sequence of events - which are critical for accurate fault assessment. Existing solutions that model time, like Dynamic Bayesian Networks (DBNs), typically suffer from computational complexity and scalability issues. 
This paper introduces a hybrid diagnostic architecture that integrates a standard Bayesian Networks (BNs) with a powerful temporal reasoner R2U2 (Realizable Responsive Unobtrusive Unit). By decoupling temporal logic from probabilistic inference, our approach allows the specialized R2U2 engine to efficiently process complex time-dependent conditions and provide nuanced inputs to the BNs. The result is a more scalable, flexible, and robust framework for diagnosing failures in systems where temporal behavior is a key factor. The paper will detail this architecture, its generation from system models, and demonstrate its capabilities using a UAV electric powertrain example.

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Chetan Kulkarni and Johann Schumann. Beyond Dynamic Bayesian Networks: Fusing Temporal Logic Monitors with Probabilistic Diagnosis (Short Paper). In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 13:1-13:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/OASIcs.DX.2025.13

Author Details

Chetan Kulkarni
  • KBR Inc, NASA Ames Research Center, Moffett Field, CA, USA
Johann Schumann
  • KBR Inc, NASA Ames Research Center, Moffett Field, CA, USA

Acknowledgements

This work was authored by employees of KBR Wyle Services, LLC under Contract No. 80ARC020D0010 with the National Aeronautics and Space Administration. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, or allow others to do so, for United States Government purposes. All other rights are reserved by the copyright owner.

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