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

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

“…As stated above, the 4 WU commutations provide multiple observations of the same features; hence, it is possible to either employ them on a single model by computing a set of statistics over such features or by building separate models in a bagging fashion [30] and by performing the classification by voting. Preliminary results showed that the former solution presents better performance [18]; hence, we adopted it in all our models.…”

“…The methodologies employed in the literature usually vary from simple regression/classification techniques such as linear regression [14] and Bayesian Networks [11] to more complex neural-network-based algorithms [7]. However, in fabric-care home appliances, the application of SSs is limited to load quantity [15][16][17][18] and humidity estimation [19,20] and the resource constrained environment makes the problem challenging and the possible solution limited to simple ones.…”

“…The most discriminative property of different laundry typologies is the quantity of water absorbed during the washing cycle; however, this is affected also by the quantity of clothes inserted in the WM. For this reason, in Reference [18], we proposed a SS for load-weight estimation that provided accurate predictions exploiting on-board sensor measurements. In this work, we assume the weight to either be known or estimated through another load-weight SS.…”

Machine Learning-Based Soft Sensors for the Estimation of Laundry Moisture Content in Household Dryer Appliances

“…As seen in Figure 10a, there still exists a problem related to the variance of estimation error in the use of Method 3, especially for load B. Some new constrains on estimated values of parameters of the cubic model (7) have been imposed in order to reduce the error variance, taking into account the fact that drying tests in laboratory are time-consuming and no more data are available for this work now. The use of a richer dataset could be a next-step of this project; here, the goal was to develop SSs using already available tests made for different projects, avoiding dedicated Design of Experiments (DOE) tests.…”

“…Few solutions based on machine learning procedures in the context of fabric care major appliances are available in the literature; for example, in [6][7][8][9] SSs based on machine learning approaches have been presented to estimate the laundry weight in washing machines. Two main issues have to be faced when developing SSs for fabric care appliances: (i) the collection of a sufficient amount of data from laboratories, where accurate measures are taken, is time-consuming; (ii) the complexity associated with ML solutions in domestic appliances.…”