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Building Heterogeneous Models at Runtime to Detect Faults in Ambient-Intelligent Environments

Abstract : This paper introduces an approach for fault detection in ambient-intelligent environments. It proposes to compute predictions for sensor values, to be compared with actual values. As ambient environments are highly dynamic, one cannot pre-determine a prediction method. Therefore, our approach relies on (a) the modeling of sensors, actuators and physical effects that link them, and (b) the automatic construction at run-time of a heterogeneous prediction model. The prediction model can then be executed on a heterogeneous modeling platform such as ModHel'X, which yields predicted sensor values.
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https://hal-supelec.archives-ouvertes.fr/hal-00905275
Contributor : Christophe Jacquet <>
Submitted on : Monday, November 18, 2013 - 9:36:59 AM
Last modification on : Wednesday, September 16, 2020 - 5:19:29 PM
Long-term archiving on: : Friday, April 7, 2017 - 11:59:47 PM

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  • HAL Id : hal-00905275, version 1

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Christophe Jacquet, Ahmed Mohamed, Frédéric Boulanger, Cécile Hardebolle, Yacine Bellik. Building Heterogeneous Models at Runtime to Detect Faults in Ambient-Intelligent Environments. MRT 2013, Sep 2013, Miami, United States. pp.52-63. ⟨hal-00905275⟩

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