Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation Chiachío Ruano, Manuel Saleh, Ali Chiachío Ruano, Juan Petri nets Model similarity Bayesian inference Approximate Bayesian Computation Maintenance models This paper is part of the ENHAnCE ITN project (https://www.h2020-enhanceitn.eu/) funded by the European Union's Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement No. 859957. The authors would like to thank the Lloyd's Register Foundation (LRF), a charitable foundation in the U.K. helping to protect life and property by supporting engineeringrelated education, public engagement, and the application of research. The authors gratefully acknowledge the support of these organizations which have enabled the research reported in this paper. The accurate modeling of engineering systems and processes using Petri nets often results in complex graph representations that are computationally intensive, limiting the potential of this modeling tool in real life applications. This paper presents a methodology to properly define the optimal structure and properties of a reduced Petri net that mimic the output of a reference Petri net model. The methodology is based on Approximate Bayesian Computation to infer the plausible values of the model parameters of the reduced model in a rigorous probabilistic way. Also, the method provides a numerical measure of the level of approximation of the reduced model structure, thus allowing the selection of the optimal reduced structure among a set of potential candidates. The suitability of the proposed methodology is illustrated using a simple illustrative example and a system reliability engineering case study, showing satisfactory results. The results also show that the method allows flexible reduction of the structure of the complex Petri net model taken as reference, and provides numerical justification for the choice of the reduced model structure. 2022-04-18T06:57:33Z 2022-04-18T06:57:33Z 2022-02-19 info:eu-repo/semantics/article Manuel Chiachío... [et al.]. Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation, Reliability Engineering & System Safety, Volume 222, 2022, 108365, ISSN 0951-8320, [https://doi.org/10.1016/j.ress.2022.108365] http://hdl.handle.net/10481/74303 10.1016/j.ress.2022.108365 eng info:eu-repo/grantAgreement/EC/H2020/859957 http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España Elsevier