The kernel principal component analysis (KPCA) method employs the first several kernel principal components (KPCs)

which indicate the most variance information of normal observations for process monitoring

but may not reflect the fault information. In this study

sensitive kernel principal component analysis (SKPCA) is proposed to improve process monitoring performance

i.e.

to deal with the discordance of T

2

statistic and squared prediction error δ

SPE

statistic and reduce missed detection rates. T

2

statistic can be used to measure the variation directly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T

2

statistic along each KPC

SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.