Fault detection and diagnosis (FDD) technology plays an important role in maintaining the stable and efficient operation of the chiller system, and screening the most important parameters for FDD is the key step that is far more critical than the selection of diagnosis methods. This study proposes a feature importance ranking method based on random forest (RF) to improve the FDD performance of a chiller while using fewer sensors, which is proved to be effective and efficient. When the product type or sensor configuration changes, the proposed method can be used to rank the feature importance efficiently based on the new fault simulation data, the FDD model using the features that are highly significant to fault indication and diagnosis can be established and the diagnosis performance can be promoted, accordingly. For verification and validation, fault simulation experiments of normal operation, refrigerant leakage and refrigerant overcharge have been carried out on a 200-ton variable-speed screw chiller. The importance of 15 parameters obtained from the experiments is ranked according to their contributions to the diagnosis of the corresponding faults using the proposed RF importance ranking method. Different critical features have also been discussed in terms of FDD performance and expertise knowledge for screw chiller. The optimal feature set for the investigated faults of the chiller is found to have just four parameters: Refrigerant Discharge Temperature (TR_dis), Condenser Water Temperature Difference (TWCD), Evaporator Water Temperature Difference (TWED) and Compressor Power (kW). The overall diagnostic accuracy reaches 99.90%, higher than using all the parameters. Fewer sensors achieve better performance.

故障检测与诊断（FDD）技术在维持冷水机组系统稳定高效运行方面发挥着重要作用，而对FDD最重要参数的筛选是远比诊断方法选择更为关键的关键步骤。本研究提出了一种基于随机森林（RF）的特征重要性排序方法，在使用较少传感器的情况下提高冷水机组的 FDD 性能，被证明是有效且高效的。当产品类型或传感器配置发生变化时，该方法可以基于新的故障模拟数据有效地对特征重要性进行排序，建立使用对故障指示和诊断具有高度意义的特征的FDD模型并进行诊断。相应地，性能可以得到提升。为了验证和确认，在一台200吨级变速螺杆式冷水机组上进行了正常运行、制冷剂泄漏和制冷剂过量充注的故障模拟实验。使用所提出的射频重要性排序方法，根据它们对相应故障诊断的贡献，对从实验中获得的 15 个参数的重要性进行排序。在 FDD 性能和螺杆式冷水机组的专业知识方面，还讨论了不同的关键特性。发现冷水机组调查故障的最佳特征集只有四个参数：制冷剂排放温度 (TR_dis)、冷凝器水温差 (TWCD)、蒸发器水温差 (TWED) 和压缩机功率 (kW)。整体诊断准确率达到99.90%，高于使用所有参数。