Chillers play essential roles in the heating, ventilation and air conditioning (HVAC) systems to ensure the required thermal comfort. To reduce the operational risk such as faulty operation or energy waste, it’s essential to develop the robust and effective fault detection and diagnosis (FDD) strategy for the chillers. This paper presents a novel hybrid reference model called multi-region XGBoost model that integrates parameter optimized XGBoost model with mean shift clustering method. Based on the reference model, an FDD strategy, including two stages, is proposed. The experiments are carried out on a screw chiller, on which three thermal faults are investigated. The indicative characteristic quantities are selected as the model inputs for detection and diagnosis purpose. The FDD result of the multi-region XGBoost model is compared with that of support vector machine (SVM) model and XGBoost model without clustering. In terms of the performance of fault detection, the multi-region XGBoost model detects 97.26% faulty samples while correctly identifies 99.10% fault-free samples. As for fault diagnosis, the multi-region XGBoost model possesses the highest fault diagnosis accuracy of 96.89%. Besides, the hybrid model also shows the best generalization ability. The FDD result reveals that the multi-region XGBoost model has the reliable efficiency for the screw chiller application.

冷水机组在供暖，通风和空调（HVAC）系统中起着至关重要的作用，以确保所需的热舒适性。为了降低操作风险，例如错误的操作或能源浪费，必须为冷水机开发可靠而有效的故障检测和诊断（FDD）策略。本文提出了一种新颖的混合参考模型，称为多区域XGBoost模型，该模型将参数优化的XGBoost模型与均值漂移聚类方法相集成。在参考模型的基础上，提出了包括两个阶段的FDD策略。实验是在螺杆式冷水机上进行的，在该冷水机上研究了三个热故障。选择指示性特征量作为模型输入，以进行检测和诊断。将多区域XGBoost模型的FDD结果与支持向量机（SVM）模型和XGBoost模型的结果进行比较，而不进行聚类。在故障检测的性能方面，多区域XGBoost模型检测97.26％的故障样本，同时正确识别99.10％的无故障样本。对于故障诊断，多区域XGBoost模型具有最高的故障诊断准确率，为96.89％。此外，混合模型还显示出最佳的泛化能力。FDD结果表明，多区域XGBoost模型对于螺杆冷却器应用具有可靠的效率。对于故障诊断，多区域XGBoost模型具有最高的故障诊断准确率，为96.89％。此外，混合模型还显示出最佳的泛化能力。FDD结果表明，多区域XGBoost模型对于螺杆冷却器应用具有可靠的效率。对于故障诊断，多区域XGBoost模型具有最高的故障诊断准确率，为96.89％。此外，混合模型还显示出最佳的泛化能力。FDD结果表明，多区域XGBoost模型对于螺杆冷却器应用具有可靠的效率。