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Fault Detection of Supermarket Refrigeration Systems Using Convolutional Neural Network

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  • Author(s): Soltani, Zahra; Soerensen, Kresten Kjaer; Leth, John; Bendtsen, Jan Dimon
  • Source:
    Soltani , Z , Soerensen , K K , Leth , J & Bendtsen , J D 2020 , Fault Detection of Supermarket Refrigeration Systems Using Convolutional Neural Network . in IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society . , 9254485 , IEEE Computer Society Press , Proceedings of the Annual Conference of the IEEE Industrial Electronics Society , pp. 231-238 , 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 , Virtual, Singapore , Singapore , 18/10/2020 . https://doi.org/10.1109/IECON43393.2020.9254485
  • Subject Terms:
    classification; convolutioal; data quality; evaporation; fault; machine learning; neural network; refrigeration
  • Document Type:
    article in journal/newspaper
  • Language:
    English
  • Additional Information
    • Publication Information:
      IEEE Computer Society Press
    • Publication Date:
      2020
    • Collection:
      Aalborg University (AAU): Publications / Aalborg Universitet: Publikationer
    • Abstract:
      The functionality of supermarket refrigeration systems (SRS) has a significant impact on the quality of food products and potentially human health. Automatic fault detection and diagnosis of SRS is desired by manufacturers and customers as performance is improved, and energy consumption and cost is lowered. In this work, Convolutional Neural Networks (CNN) are applied for fault detection and diagnosis of SRS. The network is found to be able to classify the fault with 99% accuracy. The sensitivity of the designed model to data quality is also assessed. The results show that the model can classify faults at low sample rates if the training set is large enough. Moreover, the model displays low sensitivity to data quality such as noisy and perturbed validation data, and the frequency of false positives is satisfactorily low as well.
    • File Description:
      application/pdf
    • Accession Number:
      10.1109/IECON43393.2020.9254485
    • Online Access:
      https://vbn.aau.dk/da/publications/2b63ba3f-8517-4386-92c9-a4fba9e07591
      https://doi.org/10.1109/IECON43393.2020.9254485
      https://vbn.aau.dk/ws/files/447767757/PID6554339.pdf
      http://www.scopus.com/inward/record.url?scp=85097780696&partnerID=8YFLogxK
    • Rights:
      info:eu-repo/semantics/openAccess
    • Accession Number:
      edsbas.13AD2C31