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Facilitating Fault Tree Analysis with Generative AI

  • Technical University of Munich
  • Fraunhofer Institute for Cognitive Systems

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Fault Tree Analysis (FTA) is a cornerstone safety and reliability engineering technique. However, the manual development of fault trees can be time-intensive, error-prone, and challenging for complex systems. This paper proposes a novel application of Generative AI (GenAI) to automate and enhance FTA. Instead of using LLMs to generate a complete fault tree, however, we believe that it is essential that the human analyst still drives the analysis and “only” gets support from an analysis co-pilot. By leveraging large language models (LLMs), our approach suggests new sub-causes for existing fault trees. The methodology will be applied to a Lane Keeping Assist System (LKAS) to demonstrate how GenAI can extend fault tree coverage and completeness.

Publication series

NameLecture Notes in Computer Science
Volume15955 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceCo-Design of Communication, Computing and Control in Cyber-Physical Systems, CoC3CPS 2025, 20th Workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems, DECSoS 2025, 12th International Workshop on Next Generation of System Assurance Approaches for Critical Systems, SASSUR 2025, 4th International Workshop on Safety and Security Interaction, SENSEI 2025, 2nd International Workshop on Safety/Reliability/Trustworthiness of Intelligent Transportation Systems, SRToITS 2025 and 8th International Workshop on Artificial Intelligence Safety Engineering, WAISE 2025 held in conjunction with the 44th International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2025
Country/TerritorySweden
CityStockholm
Period9/09/259/09/25

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