Fault Detection and Diagnosis for Sensors in Variable Air Volume Systems

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.