Decentralized causal-based monitoring for large-scale systems: sensitivity and robustness assessment

Ensuring safety and efficiency in industrial systems requires effective fault detection and diagnosis, which becomes increasingly challenging in high-dimensional and complex environments. Traditional multivariate statistical process monitoring methods, such as those based on Principal Component Analysis and Partial Least Squares, often fall short in their ability to diagnose localized faults due to their lack of causal modeling. This paper introduces a Causal Network-based Decentralized Multivariate Statistical Process Control (CNd-MSPC) framework, which employs causal networks and community detection—specifically the Leiden algorithm—to segment large systems into functional communities and perform distributed monitoring. This structural partitioning preserves essential causal and topological information, enhancing the sensitivity for fault detection in high-dimensional systems by allowing focused analysis of specific sub-networks. Through extensive testing with a graph-based data simulator, we demonstrate that CNd-MSPC consistently outperforms centralized methods across various network sizes, achieving higher fault detection sensitivity for both process perturbations and sensor biases, especially in large networks. The decentralized approach retains high sensitivity, even when data from several communities are missing due to process disruptions.