  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 data sets relevant. Thus, our goal with this work is to
propose a data-driven approach to infer efficient FT structures that achieve a
complete representation of the failure mechanisms contained in the failure data
set without human intervention. Our algorithm, the FT-MOEA, based on
multi-objective evolutionary algorithms, enables the simultaneous optimization
of different relevant metrics such as the FT size, the error computed based on
the failure data set and the Minimal Cut Sets. Our results show that, for six
case studies from the literature, our approach successfully achieved automatic,
efficient, and consistent inference of the associated FT models. We also
present the results of a parametric analysis that tests our algorithm for
different relevant conditions that influence its performance, as well as an
overview of the data-driven methods used to automatically infer FT models.
