The existing fault detection and diagnosis (FDD) model of chillers requires considerable normal and fault data. The acquisition of these data is time-consuming and expensive, and the model is only suitable for special units, which makes it difficult to popularize FDD technology in the operation and management of chillers. At present, a 120-ton chiller has only a small amount of normal and fault data when compared with the abundant data of a 200-ton model of the same series. This study investigates the FDD model of a 120-ton chiller and considers similar characteristics of the refrigeration cycle of the same series of chillers. A training set can be created using the 200-ton prior-knowledge data and the 120-ton data. However, this training set is imbalanced, and the common imbalanced processing synthetic minority oversampling technique (SMOTE) synthesis mechanism has an overlap problem. This study adopts two adaptive imbalance processing technologies called the adaptive synthetic sampling approach (ADASYN) and borderline SMOTE (BSM) that can solve the imbalance problem and SMOTE oversampling overlap problem during knowledge transfer. A support vector machine FDD model with 100% to 400% oversampling ratios is established. The best model is ADASYN with less than 100% oversampling ratio, with a diagnostic accuracy rate of 94.33%.

现有的冷水机故障检测与诊断（FDD）模型需要大量正常数据和故障数据。这些数据的获取既耗时又昂贵，并且该模型仅适用于特殊设备，这使得在冷却器的运行和管理中难以推广FDD技术。目前，与同系列200吨型号的大量数据相比，一台120吨的冷水机组只有少量正常和故障数据。这项研究调查了120吨冷水机的FDD模型，并考虑了相同系列冷水机制冷循环的相似特征。可以使用200吨的先验知识数据和120吨的数据创建训练集。但是，此训练集不平衡，常见的不平衡处理合成少数过采样技术（SMOTE）合成机制存在重叠问题。本研究采用两种自适应不平衡处理技术，分别称为自适应合成采样方法（ADASYN）和边界SMOTE（BSM），可以解决知识转移过程中的不平衡问题和SMOTE过采样重叠问题。建立了具有100％至400％过采样率的支持向量机FDD模型。最好的模型是ADASYN，过采样率低于100％，诊断准确率达到94.33％。本研究采用两种自适应不平衡处理技术，分别称为自适应合成采样方法（ADASYN）和边界SMOTE（BSM），可以解决知识转移过程中的不平衡问题和SMOTE过采样重叠问题。建立了具有100％至400％过采样率的支持向量机FDD模型。最好的模型是ADASYN，过采样率低于100％，诊断准确率达到94.33％。本研究采用两种自适应不平衡处理技术，分别称为自适应合成采样方法（ADASYN）和边界SMOTE（BSM），可以解决知识转移过程中的不平衡问题和SMOTE过采样重叠问题。建立了具有100％至400％过采样率的支持向量机FDD模型。最好的模型是ADASYN，过采样率低于100％，诊断准确率达到94.33％。