Fault detection and diagnosis for sensor is necessary

which affects the performance of the HVAC system seriously.The kernel principal component analysis（KPCA）effectively captures the nonlinear relationship of the process variables

which computes principal component in high-dimensional feature space by means of integral operators and nonlinear kernel functions.The KPCA method is used in diagnosing for four familiar sensor faults.At first its fault is detected by Q statistic

at second its fault is identified by T2contribution percent change.The experiment result shows the KPCA method has good performance in fault detection and diagnosis.