75 Chapter 4 Fault Tree inference using Multi-Objective Evolutionary Algorithms and Confusion Matrix-based metrics Paper published at L. A. Jimenez-Roa, N. Rusnac, M. Volk, M. Stoelinga, “Fault Tree inference using Multi-Objective Evolutionary Algorithms and Confusion Matrix-based metrics”, in Formal Methods for Industrial Critical Systems (FMICS), 2024. Springer’s Lecture Notes in Computer Science (LNCS). doi: 10.1007/978-3-031-68150-9_5. Abstract In the domain of reliability engineering and risk assessment, the development of Fault Tree (FT) models is pivotal for decision-making in complex systems. Traditional FT model development, relying on manual e!orts and expert collaboration, is both time-consuming and error-prone. The era of Industry 4.0 introduces capabilities for automatically deriving FTs from inspection and monitoring data. This chapter presents FT-MOEA-CM, an extension of the FT-MOEA algorithm (Chapter 2) for inferring FT models from failure data using multi-objective optimisation. FT-MOEA-CM enhances its predecessor by integrating confusion matrix-derived metrics and incorporating parallelisation and caching mechanisms. Our evaluation on six FTs from diverse application areas showcases that FT-MOEA-CM exhibits (1) enhanced robustness, (2) faster convergence and (3) better scalability than FT-MOEA, suggesting its potential in e"ciently inferring larger FT models.
RkJQdWJsaXNoZXIy MjY0ODMw