制冷剂充注量异常是一种高风险故障，直接影响制冷系统的性能，且其表征参数诸多，难以有效、快速、准确地在线识别。针对上述问题，文中提出了一种基于随机森林 ( ＲF) 算法的制冷剂充注量故障监测与诊断方法。使用 ASHＲAE 1999 年提供的制冷主机故障数据库，对制冷剂充注量相关的直接测量特征数据进行分析，在保持各特征变量物理意义的前提下，利用随机森林算法研究各故障特征量的贡献率，并在不同样本规模和故障特征量维度的条件下，比较了基于 ＲF、基于支持向量机 ( SVM) 、基于决策树 ( DT) 算法的制冷剂充注量故障诊断效果。结果表明: ＲF 算法具有比较好的识别效率以及较高的分类准确率，平均诊断准确率分别比 DT 算法、SVM 算法提高约 3. 3% 和 2. 9% 。此外，文中还分析了充注量异常诊断贡献率较高的前 3 个故障特征量，为保证制冷系统运行性能与安全运行提供了理论依据。

The refrigerant charge ( ＲC) breakdown of chiller is a high-risk fault that directly affects the operational efficiency of air conditioning system． Due to the mass representative parameters，it is difficult to detect the fault on- line effectively，quickly and accurately． Aiming at these problems，a fault diagnosis method based on random fo- rests ( ＲF) algorithm was proposed for the ＲC breakdown by using the fault database of refrigeration provided by ASHＲAE in 1999． Directly measured feature parameters related to ＲC were analyzed． Under the premise of retai- ning the physical meaning，contribution rate of each fault feature was studied with ＲF algorithm． Then the detection accuracy of ＲC fault diagnosis based on ＲF，support vector machines ( SVM) and decision tree ( DT) algorithms were compared under the condition of different dimensions and sample size． The results show that the ＲF algorithm has the best recognition efficiency and highest classification accuracy． Compared with DT and SVM，the average detection accuracy of ＲF increases by 3. 3% and 2. 9% ，respectively． Furthermore，three important fault charac- teristics which have a high impact on the diagnosis of ＲC were analyzed． This can provide a theoretical basis for en- suring operating performance and safe operation of refrigeration system．