Abstract: Given that decentralized non-Gaussian process monitoring approaches usually don't take into account the integrality of measured variable as a whole, we present a novel decentralized non-Gaussian process monitoring method to extract simultaneously the local feature and the global feature of the measured variables. Firstly, we modify the original FastICA iterative algorithm to derive a modified ICA algorithm (MICA); And then the extraction of block independent components can be oriented by the de-mixing vectors calculated from the measured variables as a whole, a multi-block MICA (MBMICA)algorithm that considers the integrality of the measured variables is thus obtained, based on which decentralized non-Gaussian process monitoring can then be implemented; Finally, we validate the feasibility of the MICA algorithm, and the superiority of the proposed method over other decentralized monitoring approaches through comparisons.