41 Chapter 2 Automatic Inference of Fault Tree Models via Multi-Objective Evolutionary Algorithms Paper published at L. A. Jimenez-Roa, T. Heskes, T. Tinga and M. Stoelinga, “Automatic Inference of Fault Tree Models via Multi-Objective Evolutionary Algorithms”, in IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 4, pp. 3317-3327, 1 July-Aug. 2023, doi: 10.1109/TDSC.2022.3203805. Abstract Fault Tree Analysis is a well-known technique in reliability engineering and risk assessment, which supports decision-making processes and the management of complex systems. Traditionally, Fault Tree (FT) models are built manually together with domain experts, considered a time-consuming process prone to human errors. With Industry 4.0, there is an increasing availability of inspection and monitoring data, making techniques that enable knowledge extraction from large datasets relevant. Thus, our goal with this work is to propose a data-driven approach to infer e"cient FT structures that achieve a complete representation of the failure mechanisms contained in the failure dataset without human intervention. Our algorithm, the FT-MOEA, based on multi-objective evolutionary algorithms, enables the simultaneous optimisation of di!erent relevant metrics such as the FT size, the error computed based on the failure dataset and the Minimal Cut Sets. Our results show that, for six case studies from the literature, our approach successfully achieved automatic, e"cient, and consistent inference of the associated FTmodels. We also present the results of a parametric analysis that tests our algorithm for di!erent relevant conditions that influence its performance, as well as an overview of the data-driven methods used to automatically infer FT models.
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