制冷学报2025，Vol.46Issue(2)：145-154,10.DOI:10.12465/j.issn.0253-4339.2025.02.145
基于关键特征的制冷剂泄漏故障软测量研究
Soft Measurement of Refrigerant Leakage Based on Key Features
摘要
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 1 440 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.关键词
制冷剂泄漏/特征选择/软测量/随机森林/支持向量回归Key words
refrigerant leakage/feature selection/soft measurements/random forest/support vector regression分类
通用工业技术引用本文复制引用
凌敏彬,杨钰婷,韩华,徐玲,崔晓钰..基于关键特征的制冷剂泄漏故障软测量研究[J].制冷学报,2025,46(2):145-154,10.基金项目
国家自然科学基金(51506125)资助项目.(The project was supported by the National Natural Science Foundation of China(No.51506125).) （51506125）
