The diagnosis of complex systems benefits greatly from a differential, multistep approach that narrows down the list of possible conditions or failures that share the same observable effects to a single root cause. We provide a suitable and practically applicable methodology for this. In extension to existing work, it covers all types of diagnostic actions, i.e., the observation of system properties, active testing and system interventions like providing a dedicated diagnostic input or forcing the system into discriminating states, but also the replacement of components. Combining all these possible steps into one probabilistic and causal reasoning framework, we I) stepwise generate the diagnostic model systematically to correctly cover the interplay of observations and diagnostic interventions, and II) provide decision support based on counterfactuals for the selection of the next diagnostic step, countering the vast number of possible actions that arise in machine diagnostic processes. We developed and successfully tried our methodology for diagnosing cyber-physical systems in the high-tech industry, but we found that it supports more processes, such as computing intervention actions for autonomous robots.
systematic diagnosis, probabilistic reasoning, active testing, interventions, counterfactual reasoning
Bayes Server, www.bayesserver.com, retrieved 24-03-2023.
Barbini, L., & Borth, M. (2019). Probabilistic Health and Mission Readiness Assessment at System-Level. Annual Conference of the PHM Society, 11(1).
Barbini, L., Bratosin, C., & van Gerwen, E. (2020). Model- based diagnosis in complex industrial systems. PHM Society European Conference, 5(1), 8.
Borth, M., & von Hasseln, H. (2002). Systematic generation of Bayesian networks from systems specifications. In Intelligent Information Processing: IFIP 17th World Computer Congress on Intelligent Information Processing August 25–30, 2002, Montréal, Québec, Canada 1 (pp. 155-166). Springer US.
de Kleer, J., Raiman O., Shirley, M. (1992) One Step Lookahead is Pretty Good, In: Readings in Model- Based Diagnosis, Hamscher, W., de Kleer, J. and Console, L., Morgan Kaufmann.
Heckerman, D., Mamdani, A. & Wellman, M.P. (1995). Real-world applications of Bayesian networks. Communications of the ACM 38.3: 24-26.
Jensen, F.V. & Nielsen, T.D. (2007). Bayesian Networks and Decision Graphs, Springer Verlag.
Oladyshkin, S. & Nowak, W. (2019). The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design. Entropy, vol. 21, no. 11, Art. no. 11, Nov. 2019, doi: 10.3390/e21111081.
Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. Artificial intelligence, 29(3), 241-288.
Pearl, J. (2009). Causality. Cambridge University Press. Pearl, J. & Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books.
Ricks, B. W., & Mengshoel, O. J. (2009). Methods for probabilistic fault diagnosis: An electrical power system case study. Annual Conference of the Prognostics and Health Management Society. TNO (2022). Situational awareness in robot dogs. www.tno.nl/en/digital/artifical-intelligence/safe- autonomous-systems/situational-awareness-robot-dogs/
This work is licensed under a Creative Commons Attribution 3.0 Unported License.