Automatic Detection and Diagnosis of faults in Sensors used in EMS

A. Taal, Laure Itard, W. Zeiler, Y Zhao

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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    Abstract

    A much occurring problem in the Energy Management Systems of existing buildings and HVAC services is that the measurements are unreliable. In this article a methodology is described which can be used to determine the presence of errors in energy monitoring, caused by faulty measurements. These errors can be detected and subsequently diagnosed. Detection of monitoring errors is done based on occurring symptoms. Determination of these symptoms is done using the laws of conservation of energy, mass and pressure. The diagnosis is done by using a statistical method based on Bayesian theory in which the chance of an error occurring is determined based on ( combinations of) the symptoms. The method is built in a Bayesian Belief Network (BBN) software tool. The advantage of BBN is that it is consistent with the working methods of experts in installation technology.
    Original languageEnglish
    Title of host publicationCLIMA 2016
    Subtitle of host publicationProceedings of the 12th REHVA World Congress
    EditorsPer Kvols Heiselberg
    PublisherAalborg University
    Pages1-10
    Publication statusPublished - 2016
    EventCLIMA 2016 - 12th REHVA World Congress - Aalborg, Denmark
    Duration: 22 May 201625 May 2016
    http://vbn.aau.dk/en/activities/clima-2016--12th-rehva-world-congress(43019fd3-70a7-4c5c-9176-825addd5913f).html

    Conference

    ConferenceCLIMA 2016 - 12th REHVA World Congress
    CountryDenmark
    CityAalborg
    Period22/05/1625/05/16
    Internet address

    Keywords

    • FDD
    • Bayesian method
    • BBN
    • fault detection
    • fault diagnosis
    • systems theory
    • Building Energy Management System
    • Energy Monitoring System
    • sensor faults
    • model faults, HVAC equipment

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