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Fault Trees from Data: Efficient Learning with an Evolutionary Algorithm

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Dependable Software Engineering. Theories, Tools, and Applications (SETTA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11951))

Abstract

Cyber-physical systems come with increasingly complex architectures and failure modes, which complicates the task of obtaining accurate system reliability models. At the same time, with the emergence of the (industrial) Internet-of-Things, systems are more and more often being monitored via advanced sensor systems. These sensors produce large amounts of data about the components’ failure behaviour, and can, therefore, be fruitfully exploited to learn reliability models automatically. This paper presents an effective algorithm for learning a prominent class of reliability models, namely fault trees, from observational data. Our algorithm is evolutionary in nature; i.e., is an iterative, population-based, randomized search method among fault-tree structures that are increasingly more consistent with the observational data. We have evaluated our method on a large number of case studies, both on synthetic data, and industrial data. Our experiments show that our algorithm outperforms other methods and provides near-optimal results.

This research is supported by the Dutch Technology Foundation (STW) under the Robust CPS program (project 12693), the EU project SUCCESS, the Smart Industries program (project SEQUOIA 15474), and the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.

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Notes

  1. 1.

    See http://arxiv.org/abs/1909.06258.

  2. 2.

    https://gitlab.science.ru.nl/alinard/learning-ft.

  3. 3.

    https://dftbenchmarks.utwente.nl/.

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Authors and Affiliations

  1. Institute for Computing and Information Science, Radboud University, Nijmegen, The Netherlands

    Alexis Linard & Mariëlle Stoelinga

  2. University of Twente, Enschede, The Netherlands

    Doina Bucur & Mariëlle Stoelinga

  3. KTH Royal Institute of Technology, Stockholm, Sweden

    Alexis Linard

Authors
  1. Alexis Linard
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  2. Doina Bucur
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Correspondence to Alexis Linard .

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Editors and Affiliations

  1. Hong Kong Polytechnic University, Kowloon, Hong Kong

    Nan Guan

  2. RWTH Aachen University, Aachen, Germany

    Joost-Pieter Katoen

  3. Singapore Management University, Singapore, Singapore

    Jun Sun

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Linard, A., Bucur, D., Stoelinga, M. (2019). Fault Trees from Data: Efficient Learning with an Evolutionary Algorithm. In: Guan, N., Katoen, JP., Sun, J. (eds) Dependable Software Engineering. Theories, Tools, and Applications. SETTA 2019. Lecture Notes in Computer Science(), vol 11951. Springer, Cham. https://doi.org/10.1007/978-3-030-35540-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-35540-1_2

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