Fault Tree Inference Using Multi-objective Evolutionary Algorithms and Confusion Matrix-Based Metrics

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 efforts 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 paper presents FT-MOEA-CM, an extension of the FT-MOEA algorithm for inferring FT models from failure data using multi-objective optimization. FT-MOEA-CM enhances its predecessor by integrating confusion matrix-derived metrics and incorporating parallelization 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 efficiently inferring larger FT models.