تخمين وزن بار ماشين لباسشويي با استفاده از روش حسگر نرم بر پايه يادگيري ماشين

The aim of this study is to develop a data-driven method for estimating laundry weight in washing machines using machine learning algorithms, without the need for external sensors. Although conventional sensor-based methods can provide acceptable accuracy, they tend to be costly, vulnerable, an‎d require periodic calibration. In contrast, the proposed approach in this research relies on internal washing machine data an‎d the design of an intelligent soft sensor. For data collection, over 1,120 real-world tests were conducted on a direct drive washing machine using various fabric an‎d clothing loads. Motor current, instantaneous speed, an‎d motor stop duration were recorded as initial input variables. Then, using feature extraction an‎d selec‎tion methods, the most influential variables were identified an‎d redundant data was eliminated. In the modeling phase, several machine learning algorithms including Ran‎dom Forest, XGBoost, Gradient Boosting, an‎d Logistic Regression were tested. The results showed that the Logistic Regression model, when optimized, achieved an accuracy of 97% while remaining feasible for implementation on stan‎dard washing machine hardware. This presents a key advantage over studies utilizing deep neural networks, which typically require additional hardware an‎d high-performance processors. Overall, this study, by leveraging real-world data, eliminating the dependency on external sensors, an‎d selec‎ting lightweight models, successfully addresses some of the main gaps in previous research an‎d offers a cost-effective, sustainable, an‎d generalizable approach to estimating laundry weight.

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