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For the purpose of monitoring large-scale distributed processes, a double layer fault detection method based on hierarchical multi-block decomposition is proposed. The process variables are first divided into multiple blocks using mutual information-based hierarchical decomposition. The Gaussian and non-Gaussian information in each block are divided into two layers using the partial least squares and a non-Gaussian regression method, respectively. The corresponding monitoring statistics constructed for both the Gaussian and non-Gaussian components are then integrated using the support vector data description. The performance of the proposed method is demonstrated by the application studies to a numerical example and the Tennessee Eastman (TE) benchmark process. The results show that the superiority of the proposed method over conventional methods.

Double Layer Distributed Process Monitoring Based on Hierarchical Multi-Block Decomposition