Machine Learning-based Laundry Weight Estimation for Vertical Axis Washing Machines

In laundry treatment appliances, the weight of the laundry loaded by the user inside the drum dramatically affects the operating behavior. Therefore, it is important to obtain a good estimate of the said quantity in order to correctly configure the machine before the washing/drying starts. In Vertical Axis Washing Machines the laundry weight is computed by exploiting the quantity of water absorbed by the clothes. However, such approach does not grant accurate results because the water absorption depends on the clothes fabric. For this reason, we propose a Soft Sensing approach for weight estimation that exploits the information obtained from physical sensors available on board without added costs. Data-driven Soft Sensors are developed, where, using Machine Learning techniques, a statistical model of the phenomenon of interest is created from a set of sample data.