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Laundry Fabric Classification in Vertical Axis Washing Machines Using Data-Driven Soft Sensors

Author

Listed:
  • Marco Maggipinto

    (Department of Information Engineering, University of Padova, 35131 Padova, Italy)

  • Elena Pesavento

    (Electrolux Italia S.p.a., PN 33080 Porcia, Italy)

  • Fabio Altinier

    (Electrolux Italia S.p.a., PN 33080 Porcia, Italy)

  • Giuliano Zambonin

    (Department of Information Engineering, University of Padova, 35131 Padova, Italy
    Electrolux Italia S.p.a., PN 33080 Porcia, Italy)

  • Alessandro Beghi

    (Department of Information Engineering, University of Padova, 35131 Padova, Italy
    Human Inspired Technology Research Centre, University of Padova, 35121 Padova, Italy)

  • Gian Antonio Susto

    (Department of Information Engineering, University of Padova, 35131 Padova, Italy
    Human Inspired Technology Research Centre, University of Padova, 35121 Padova, Italy)

Abstract

Embedding household appliances with smart capabilities is becoming common practice among major fabric-care producers that seek competitiveness on the market by providing more efficient and easy-to-use products. In Vertical Axis Washing Machines (VA-WM), knowing the laundry composition is fundamental to setting the washing cycle properly with positive impact both on energy/water consumption and on washing performance. An indication of the load typology composition (cotton, silk, etc.) is typically provided by the user through a physical selector that, unfortunately, is often placed by the user on the most general setting due to the discomfort of manually changing configurations. An automated mechanism to determine such key information would thus provide increased user experience, better washing performance, and reduced consumption; for this reason, we present here a data-driven soft sensor that exploits physical measurements already available on board a commercial VA-WM to provide an estimate of the load typology through a machine-learning-based statistical model of the process. The proposed method is able to work in a resource-constrained environment such as the firmware of a VA-WM.

Suggested Citation

  • Marco Maggipinto & Elena Pesavento & Fabio Altinier & Giuliano Zambonin & Alessandro Beghi & Gian Antonio Susto, 2019. "Laundry Fabric Classification in Vertical Axis Washing Machines Using Data-Driven Soft Sensors," Energies, MDPI, vol. 12(21), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4080-:d:280433
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    References listed on IDEAS

    as
    1. Sue Bowden & Avner Offer, 1994. "Household appliances and the use of time: the United States and Britain since the 1920s," Economic History Review, Economic History Society, vol. 47(4), pages 725-748, November.
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