Multiblock kernel principal component analysis (MBKPCA) has been proposed to isolate the faults and avoid the high computation cost. However

MBKPCA is not available for dynamic processes. To solve this problem

recursive MBKPCA is proposed for monitoring large scale processes. In this paper

we present a new recursive MBKPCA (RMBKPCA) algorithm

where the adaptive technique is adopted for dynamic characteristics. The proposed algorithm reduces the high computation cost

and is suitable for online model updating in the feature space. The proposed algorithm was applied to an industrial process for adaptive monitoring and found to efficiently capture the time-varying and nonlinear relationship in the process variables.