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

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.

Device Interaction Graph: Directed Decision Graph for Settings Auto-Completion

“…Another solution for estimating the cleanliness of the clothes using inputs such as amount of washing powder, duration of the wash and washing temperature is explained in [11]. A ML based approach for classifying the fabrics inside the WM is described in [12]. Another ML based approach for estimating the moisture of clothes loaded in WM is discussed in [13].…”

Soft Sensors: Software Development Applied to Aerospace Engineering Problems

“…SS can be also divided into two groups: model driven SS and data driven SS, as explains Maggipinto (2019). According to author, the first group is based on real-time analysis, allowing short-time predictions and fast decision making by managers or technicians.…”

Towards the Optimal Performance of Washing Machines Using Fuzzy Logic

“…Most WMs have been found to operate at low speeds, and although they do not measure the water level with a sensor, they must measure the level of inclination by weight. Furthermore, some WMs do not have temperature sensors to detect overheating of the system, and wash times are manually set by hand by either using personal trial or error experience [3,4].…”

“…As there are many types of fuzzy logic controllers, the most commonly used fuzzy inference system is the so-called Mamdani method given by its inventor, Professor Ebrahim Mamdani. e Mamdani fuzzy inference system comprises four steps: Fuzzification of the input variables, rule evaluation with aggregation of the rule outputs, and finally defuzzification [1,3]. Fuzzy logic systems help computers make decisions that resemble human behavior.…”