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Two methods based on Regression are presented to determine the moisture content of items, e.g. clothes and the like, which are introduced in a household laundry dryer appliance. The aim of this work is to develop Soft Sensors (SS) for a household Heat Pump Washer-Dryer (WD-HP) to provide an estimation of the desired signal (the laundry moisture during drying) avoiding the use of additional physical sensors with the goal of improving the current performance in terms of precision and energy consumption of the automatic drying cycle end and using the machine equipment already available. On an algorithmic point of view, the SS developed in this work exploits regularization methods and Genetic Programming for Symbolic Regression in order to find suitable models for the purpose at hand. Proposed approaches have been tested on real data provided by an industrial partner.

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.

The aim is to develop soft sensors (SSs) to provide an estimation of the laundry moisture of clothes introduced in a household Heat Pump Washer–Dryer (WD-HP) appliance. The developed SS represents a cost-effective alternative to physical sensors, and it aims at improving the WD-HP performance in terms of drying process efficiency of the automatic drying cycle. To this end, we make use of appropriate Machine Learning models, which are derived by means of Regularization and Symbolic Regression methods. These methods connect easy-to-measure variables with the laundry moisture content, which is a difficult and costly to measure variable. Thanks to the use of SSs, the laundry moisture estimation during the drying process is effectively available. The proposed models have been tested by exploiting real data through an experimental test campaign on household drying machines.

Fabric care home appliances are pervasive inhouses worldwide and manufactures are constantly working for improving product performance, efficiency, and usability. From a manufacturing perspective, increase of performancehas to be attained while minimizing the increase of production costs. In this context, a Soft Sensor for estimating the clothes load weight in a horizontal axis household washing machines ishere presented. The proposed Soft Sensor is based on Machine Learning approaches. Several methodologies, both time-seriesand feature-based, are employed and compared. The approach has been tested on real world data on commercial household washing machines.

In laundry treatment appliances, the weight ofthe laundry loaded by the user inside the drum dramaticallyaffects the operating behavior. Therefore, it is important toobtain a good estimate of the said quantity in order tocorrectly configure the machine before the washing/dryingstarts. In Vertical Axis Washing Machinesthe laundry weightis computed by exploiting the quantity of water absorbed bythe clothes. However, such approach does not grant accurateresults because the water absorption depends on the clothesfabric. For this reason, we propose a Soft Sensing approachfor weight estimation that exploits the information obtainedfrom physical sensors available on board without added costs.Data-driven Soft Sensors are developed, where, using MachineLearning techniques, a statistical model of the phenomenon ofinterest is created from a set of sample data.

The laundry weight of the loaded in the drum of a laundry treatment machine is an important piece of information; laundry weight can be used to set various washing/drying cycle parameters and to optimize performances and efficiency. Unfortunately, dedicated weight sensors cannot be included in consumer laundry equipment given the related costs. For this reason, we present in this work a soft sensor approach for estimating laundry weight based on sensors already in place in a laundry treatment equipment; in particular, we consider here a heat pump tumble dryer as case study. The proposed soft sensor is based on regularization, a popular approach in Machine Learning to provide models without overfitting the training data. Different studies are provided in this work, by considering different constrains on timing and complexity of the Soft Sensor solution. The developed Soft Sensors have been tested on laboratory data provided by an industrial partner.

The invention relates to a method for the determination of a laundry weight in a laundry treatment appliance comprising: Selecting a laundry program in the laundry treatment appliance; Starting the selected laundry program; Sensing a plurality of parameters indicating operating conditions of the laundry treatment appliance during the laundry program; and Predicting a weight of the laundry present within the laundry treatment appliance based on said plurality of parameters by means of a data-driven soft sensor.