@article{Ling2025, 
author = {Minbin Ling and Yuting Yang and Hua Han and Ling Xu and Xiaoyu Cui},
title = {Soft Measurement of Refrigerant Leakage Based on Key Features},
year = {2025},
journal = {Journal of Refrigeration},
volume = {46},
number = {2},
pages = {145-154},
keywords = {refrigerant leakage, feature selection, soft measurements, random forest, support vector regression},
url = {https://www.sciopen.com/article/10.12465/j.issn.0253-4339.2025.02.145},
doi = {10.12465/j.issn.0253-4339.2025.02.145},
abstract = {Refrigerant leakage is a frequent and costly fault that deteriorates the normal operation of a chiller; however, it is difficult to measure directly. This study proposes a data mining-and key-feature-based approach for the soft measurement of refrigerant leakage. Random forest importance ranking and distance correlation coefficients were used to select the characteristic features, and a support vector regression (SVR) soft measurement model was established to measure leakage quantitatively. The proposed model was validated through a leakage experiment conducted on a screw chiller with a rated cooling capacity of 1440 kW and a refrigerant charge of 330 kg. The results showed that the SVR soft measurement model established on the three selected key features achieved significantly improved performance. The model had a root mean square error (RMSE) of 0.844 kg and a mean absolute error (MAE) of 0.734 kg, outperforming the other three feature subsets.}
}