Various faults may occur in the air conditioning systems due to improper installation and poor maintenance. Various fault detection and diagnosis methods have been developed, which need lots of data to evaluate the protocols. However, experimental data is usually not sufficient. The FDD protocols especially machine learning based can easily overfit the limited experiment data. It may be not satisfied for the real applications because of wider range of operation. The machine learning enhanced inverse modeling method is presented to generate the simulation data under various conditions of different scenarios. The clustering algorithm is used to classify the training data reasonably balancing the weights of different conditions. The particle swarm optimization (PSO) is developed to obtain the global optimal estimation of model parameters under wider operation conditions. The experimental data of both variable and constant speed systems are used to validate clustering-PSO enhanced algorithm, which shows acceptable capability and accuracy of prediction.

由于安装不当和维护不善，空调系统可能会发生各种故障。已经开发了各种故障检测和诊断方法，这些方法需要大量数据来评估协议。但是，实验数据通常不足。FDD协议（尤其是基于机器学习的协议）可以轻松地拟合有限的实验数据。由于操作范围更广，实际应用可能无法满足要求。提出了机器学习增强的逆建模方法，以在不同场景的各种条件下生成仿真数据。聚类算法用于对训练数据进行分类，以合理地平衡不同条件下的权重。开发粒子群优化（PSO）以在更宽的操作条件下获得模型参数的全局最优估计。变速和恒速系统的实验数据均用于验证聚类-PSO增强算法，该算法显示出可接受的预测能力和准确性。