Abstract
Industries with safety-critical systems increasingly collect data on events occurring at the level of system components, thus capturing instances of system failure or malfunction. With data availability, it becomes possible to automatically learn a model describing the failure modes of the system, i.e., how the states of individual components combine to cause a system failure. We present LIFT, a machine learning method for static fault trees directly out of observational datasets. The fault trees model probabilistic causal chains of events ending in a global system failure. Our method makes use of the Mantel-Haenszel statistical test to narrow down possible causal relationships between events. We evaluate LIFT with synthetic case studies, show how its performance varies with the quality of the data, and discuss practical variants of LIFT.
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Notes
- 1.
Code can be found at https://github.com/M-Nauta/LIFT.
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Acknowledgements
This research was supported by the Dutch STW project SEQUOIA (grant 15474). The authors would like to thank Joost-Pieter Katoen and Djoerd Hiemstra for valuable feedback.
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Nauta, M., Bucur, D., Stoelinga, M. (2018). LIFT: Learning Fault Trees from Observational Data. In: McIver, A., Horvath, A. (eds) Quantitative Evaluation of Systems. QEST 2018. Lecture Notes in Computer Science(), vol 11024. Springer, Cham. https://doi.org/10.1007/978-3-319-99154-2_19
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