LIFT: Learning Fault Trees from Observational Data

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    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.
    Original languageEnglish
    Title of host publicationQuantitative Evaluation of Systems
    Subtitle of host publication15th International Conference, QEST 2018, Beijing, China, September 4-7, 2018, Proceedings
    EditorsAnnabelle McIver, Andras Horvath
    ISBN (Electronic)978-3-319-99154-2
    ISBN (Print)978-3-319-99153-5
    Publication statusPublished - 2018
    Event15th International Conference on Quantitative Evaluation of Systems, QEST 2018 - UCAS Campus, Beijing, China
    Duration: 4 Sept 20187 Sept 2018
    Conference number: 15

    Publication series

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


    Conference15th International Conference on Quantitative Evaluation of Systems, QEST 2018
    Abbreviated titleQEST 2018
    Internet address


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