A chemical process fault monitoring algorithms based on kernel entropy component analysis (KECA) is presented for the complexity and nonlinear of industrial chemical process data. The number of principal components selected by the KECA algorism is much less than the KPCA algorism

which can effectively reduce computational complexity. This is achieved by selections onto eigenvalue and eigenvector based on the value of Renyi entropy. Research shows that KECA reveals angular structure relating to the Renyi entropy of the input space data set. A new statistic—Cauchy-Schwarz divergence measure

namely the cosine value between vectors in kernel space

is proposed

which describes the similarity between different PDFs (probability density functions). It is shown that KECA has great advantages in detection latency and fault detection rate in comparing to KPCA by applying them to TE (Tennessee Eastman) process respectively.