用参数自整定模型在线检测空气处理机组故障

大型现代建筑大都安装了能源管理与控制系统（EMCS）,EMCS系统储存的大量监控数据为空调系统的在线故障检测与诊断提供了方便。提出了一种利用参数自整定空调部件模型在线检测变风量空气处理机组故障的方法。利用遗传算法优化模型参数使模型预测数据与实测值数据的残差最小,因此空调部件模型有较高的预测精度。若模型预测数据与实测数据的残差超出了预先设定的阈值,就意味着变风量空气处理机组可能存在故障。针对在实际应用时确定故障检测阈值的困难,给出了用统计方法确定阈值的方法。故障检测方法在真实建筑中进行了应用和验证,结果表明该故障检测方法可以结合EMCS系统准确有效的检测变风量空气处理机组故障。

Application of Self-tuning Models to Air Handling Units for Fault Detection

Building management control systems（BMCS） are widely employed in modern buildings.The huge amount of data available on central stations and outstations provide rich information for fault diagnosis of HVAC systems.An online fault diagnosis method for variable air volume air handling units was presented using self-tuning HVAC component models.The model parameters are tuned online by using a genetic algorithm（GA） which minimizes the error between measured and estimated performance data,so high modeling accuracy is assured.If the error between measured and estimated performance data exceeds preset thresholds,it means the occurrence of faults or abnormalities in the air handling unit system.The statistical method of selecting thresholds also is presented.The fault detection method was tested and validated using data collected from real HVAC systems.The results of validation show that the fault detection method can be integrated in BMCS systems to detect faults in air handling unit systems efficiently.