61 Chapter 3 Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularisation Paper published at L. A. Jimenez-Roa, M. Volk, M. Stoelinga, “Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularisation”, in Trapp, M., Saglietti, F., Spisländer, M., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2022. Lecture Notes in Computer Science, vol 13414. Springer, Cham. doi: 10.1007/978-3-031-14835-4_4. Abstract We present SymLearn, a method to automatically infer Fault Tree (FT) models from data. SymLearn takes as input failure data of the system components and exploits evolutionary algorithms to learn a compact FT matching the input data. SymLearn achieves scalability by leveraging two common phenomena in FTs: (i) We automatically identify symmetries in the failure dataset, learning symmetric FT parts only once. (ii) We partition the input data into independent modules, subdividing the inference problem into smaller parts. We validate our approach via case studies, including several truss systems, which are symmetric structures commonly found in infrastructures, such as bridges. Our experiments show that, in most cases, the exploitation of modules and symmetries accelerates the FT inference from hours to under three minutes. 3.1 Introduction Fault Tree Analysis (FTA) (NASA, 2002; Ruijters and Stoelinga, 2015) is one of the most prominent methods in reliability engineering, used on a daily basis by thousands of engineers. Fault Trees (FTs) are a graphical model describing how failures occurring in (atomic) system components propagate through a system and eventually lead to an overall system failure. The quantitative and qualitative
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