The proposed method maps original process variables space into feature space to deal with nonlinearities.. A KPLS model is used to build the linear relationship between kernel and output matrices. The kernel matrix is decomposed into two orthogonal parts by singular value decomposition. The statistics for each parts are determined appropriately for the purpose of quality-related fault detection. The proposed method has a more simple diagnosis logic and more stable performance.