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Hauptverfasser: De Fazio, Roberta, Marrone, Stefano, Verde, Laura, Reccia, Vincenzo, Valletta, Paolo
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2403.13785
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author De Fazio, Roberta
Marrone, Stefano
Verde, Laura
Reccia, Vincenzo
Valletta, Paolo
author_facet De Fazio, Roberta
Marrone, Stefano
Verde, Laura
Reccia, Vincenzo
Valletta, Paolo
contents One of the most appreciated features of Fault Trees (FTs) is their simplicity, making them fit into industrial processes. As such processes evolve in time, considering new aspects of large modern systems, modelling techniques based on FTs have adapted to these needs. This paper proposes an extension of FTs to take into account the problem of Predictive Maintenance, one of the challenges of the modern dependability field of study. The paper sketches the Predictive Fault Tree language and proposes some use cases to support their modelling and analysis in concrete industrial settings.
format Preprint
id arxiv_https___arxiv_org_abs_2403_13785
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Towards an extension of Fault Trees in the Predictive Maintenance Scenario
De Fazio, Roberta
Marrone, Stefano
Verde, Laura
Reccia, Vincenzo
Valletta, Paolo
Machine Learning
One of the most appreciated features of Fault Trees (FTs) is their simplicity, making them fit into industrial processes. As such processes evolve in time, considering new aspects of large modern systems, modelling techniques based on FTs have adapted to these needs. This paper proposes an extension of FTs to take into account the problem of Predictive Maintenance, one of the challenges of the modern dependability field of study. The paper sketches the Predictive Fault Tree language and proposes some use cases to support their modelling and analysis in concrete industrial settings.
title Towards an extension of Fault Trees in the Predictive Maintenance Scenario
topic Machine Learning
url https://arxiv.org/abs/2403.13785