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.
Highlights
Household appliances became extremely popular during the last century thanks to mass production and the consequent affordable prices [1]
To obtain a statistically significant performance estimation, we employed Monte Carlo Cross Validation (MCCV) [31] with 100 different test/train splits used to test the performance and 100 train/validation splits used for hyperparameter tuning
We proposed a data-driven soft sensor to detect the laundry typology in Vertical Axis Washing Machines (VA-WM)
Summary
Household appliances became extremely popular during the last century thanks to mass production and the consequent affordable prices [1]. Efficiency and usability of such appliances have dramatically improved during the years; there are still many challenges for manufacturers, especially in the context of sustainability and user experience of such products. Strict environmental laws push manufacturers to develop innovative solutions to limit the impact of their product on the environment. Both in the European and American markets, it is mandatory to apply an Energy Label/Energy Star sticker on every product that indicates its energy efficiency so that customers can make an informed choice of the products their are buying. Energy Label/Energy Star influences consumer choice in making a purchase [3], making it extremely important for the manufacturers to get high scores on such rankings
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