According to the shortcoming of total projection to latent structure (T-PLS) algorithm which has a higher false alarm rate and a higher missing report rate when it is used to detect fault in the nonlinear process

a new algorithm which is a combination of kernel function and T-PLS (KT-PLS) is proposed in this paper. Firstly

the low dimension process data is projected to the high dimension feature space through kernel function

which can resolve the nonlinear problem.Secondly

the feature space is divided into four subspaces by the guiding of quality variable.These four subspaces include the quality directly correlated subspace

the quality orthogonal subspace

the quality uncorrelated subspace and the residual subspace. Finally

statistics D and Q are used to detect fault respectively.Applying this algorithm to the Tennessee Eastman process (TEP)

the simulation results of various fault modes show that KT-PLS is better than T-PLS in monitoring strong nonlinear system.