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  <doc>
    <id>1497</id>
    <completedYear>2021</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>165</pageFirst>
    <pageLast>174</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>TIB Open Publishing</publisherName>
    <publisherPlace>Hannover</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Comparison Study of Data-Driven Anomaly Detection Approaches for Industrial Chillers</title>
    <abstract language="eng">Faults in industrial chiller systems can lead to higher energy consumption, increasing wear of system components and shorten equipment life. While they gradually cause anomalous system operating conditions, modern automatic fault detection models aim to detect them at low severity by using real-time sensor data. Many scientific contributions addressed this topic in the past and presented data-driven approaches to detect faulty system states. Although many promising results were presented to date, there is lack of suitable comparison studies that show the effectiveness of the proposed models by use of data stemming from different chiller systems. Therefore this study aims at detecting a suitable data-driven approach to detect faults reliable in different domains of industrial chillers. Thus, a unified procedure is developed, to train all algorithms in an identical way with same data-basis. Since most of the reviewed papers used only one dataset for training and testing, the selected approaches are trained and validated on two different datasets from real refrigeration systems. The data-driven approaches are evaluated based on their accuracy and true negative rate, from which the most suitable approach is derived as a conclusion.</abstract>
    <parentTitle language="eng">2nd German-West African Conference on Sustainable, Renewable Energy Systems (SusRES2021)</parentTitle>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-14970</identifier>
    <enrichment key="opus.import.date">2021-09-13T09:00:07+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="opus.import.file">filename=phpU5DbGK</enrichment>
    <enrichment key="opus.import.checksum">b540a55c7831ca9b70a3b157c0ed5393</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.52825/thwildauensp.v1i.33</enrichment>
    <enrichment key="SourceTitle">Falk, C., van de Sand, R., Corasaniti, S., &amp; Reiff-Stephan, J. (2021). A Comparison Study of Data-Driven Anomaly Detection Approaches for Industrial Chillers. In J. Reiff-Stephan, K. ́santa Amouzou, &amp; A. Adanlete, 2nd German-West African Conference on Sustainable, Renewable Energy Systems (SusRES2021) (pp. 165–174). Hannover: TIB Open Publishing. doi:10.52825/thwildauensp.v1i.33</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Constantin Falk</author>
    <author>Ron van de Sand</author>
    <author>Sandra Corasaniti</author>
    <author>Jörg Reiff-Stephan</author>
    <series>
      <title>TH Wildau Engineering and Natural Sciences Proceedings</title>
      <number>1</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fault detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>refrigeration system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data-driven machine-learning</value>
    </subject>
    <collection role="ddc" number="621">Angewandte Physik</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="Tagungsbaende" number="">German-West African Conference on Sustainable, Renewable Energy Systems SusRES</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1497/33-836-1-10-20210615.pdf</file>
  </doc>
</export-example>
