Automatic fault diagnosis is becoming increasingly important for assessing a chiller’s degradation state and plays a key role in modern maintenance strategies. Data-driven approaches have already become well established for this purpose as they rely on historical data and are therefore more generally applicable compared to their model-based counterparts. Existing chiller fault diagnosis models, however, require labelled data from the target system, which are often not available. Therefore, in this paper, a data-driven fault diagnosis model is proposed that deploys domain adaptation techniques to enable the transfer of knowledge amongst heterogeneous chillers. In particular, the model utilizes transfer component analysis (TCA) and a support vector machine with adapting decision boundaries (SVM-AD) to diagnose faults by aggregating labelled source and unlabelled target domain data in the training phase. Furthermore, it is demonstrated how the model parameters can be tuned to ensure effective classification performance, which is then evaluated by use of fault data stemming from different chiller types. Experimental results show that with the proposed approach faults can be diagnosed with high accuracy for cases when labelled target domain data are not available.

自动故障诊断对于评估冷却器的退化状态变得越来越重要，并且在现代维护策略中起着关键作用。为此，基于数据的方法已经非常成熟，因为它们依赖于历史数据，因此与基于模型的方法相比，它们具有更广泛的适用性。但是，现有的冷水机故障诊断模型需要目标系统提供带标签的数据，而这些数据通常不可用。因此，在本文中，提出了一种数据驱动的故障诊断模型，该模型采用域自适应技术来实现异构冷却器之间的知识转移。特别是，该模型利用传输成分分析（TCA）和支持决策边界的支持向量机（SVM-AD）通过在训练阶段汇总标记的源和未标记的目标域数据来诊断故障。此外，还演示了如何调整模型参数以确保有效的分类性能，然后使用来自不同类型冷水机组的故障数据对模型参数进行评估。实验结果表明，在没有标记目标域数据的情况下，采用该方法可以对故障进行高精度诊断。