【摘要】 在建立变风量空调系统仿真器的基础上,针对系统的温度、流量和压力传感器等进行故障的检测与诊断．提出基于主成分分析的方法,根据系统正常的历史运行数据建立数理统计模型,通过传感器实测数据与正常数据在故障子空间投影的比较,对传感器的故障进行在线检测;提出联合角度法通过比较新发故障向量与故障库中各经验故障向量在两个子空间投影的夹角,在线分离出故障源．结果表明,提出的主成分分析和联合角度的方法对于变风量空调系统传感器故障有较为准确和快速的检测和诊断效果．更多还原

【Abstract】 Principal component analysis (PCA) and joint angle method are presented to detect and diagnose fixed bias of temperature, flow rate and pressure sensors on the basis of variable air volume (VAV) simulator developed. According to the normal history data of the system, PCA model is set up to detect the sensor faults by comparing the projection on the residual subspace to which the real measurement vector and normal vector are projected. In addition, joint angle method is used to isolate the fault source on line through comparing the angles of the new fault vector and the known ones in both subspaces. The simulation tests show that the method can detect and diagnose the sensor faults in VAV systems accurately and rapidly.更多还原