Causal network inference and functional decomposition for decentralized statistical process monitoring: Detection and diagnosis

We propose a new systematic approach for conducting decentralized SPM based on the functional decomposition of the system's causal network. The methodology consists of first inferring the causal net-work from normal operating data of the system under study, after which the functional modules are iden-tified by exploring the graph topology and finding the strongly connected "communities". The interaction between functional modules is also taken into account (macro-causality), by extending the original com-munities with the Markov-blankets of the connection nodes, giving rise to "extended communities". Two hierarchical monitoring schemes are proposed for distributed monitoring: CNET-C (Causal Network -Centralized) and CNET-D (Causal Network-Distributed). Results demonstrate the increased sensitivity in fault detection of the proposed methodologies compared to conventional non-causal methods and cen-tralized causal methods that monitor the complete network. The proposed approaches also lead to a more effective, unambiguous, and conclusive fault diagnosis activity.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).