Automatic inference of fault tree models via multi-objective evolutionary algorithms

Lisandro Arturo Jimenez-Roa*, Tom Heskes, Tiedo Tinga, Mariëlle I.A. Stoelinga

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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 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.
Original languageEnglish
Number of pages12
JournalIEEE transactions on dependable and secure computing
Publication statusSubmitted - 24 Sep 2021

Keywords

  • Fault tree analysis
  • evolutionary algorithms
  • multi-objective optimization
  • complex systems
  • model learning
  • parametric analysis

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