Conventional multivariate statistical process monitoring methods such as Principal Component Analysis perform poorly in detecting faults in large systems. Partitioning the system and implementing a multivariate statistical process monitoring method in a distributed manner improves monitoring performance. A simulation optimization method is proposed whose objective is to find the system decomposition for which the performance of a distributed multivariate statistical process monitoring method is optimal. The proposed method uses the search strategy used in agglomerative clustering in finding the optimal system decomposition. To demonstrate its effectiveness, the proposed method is incorporated into a distributed principal component analysis based monitoring scheme and applied to the benchmark Tennessee Eastman Process case study.

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Two methods are proposed in this article to address distributed data-driven process monitoring. The first method selects sensors and allocates these sensors among subsystems with the objective of optimizing the diagnostic performance of a pattern recognition method when it is implemented in a distributed configuration subject to constraints that are imposed to meet operational requirements. The second method finds the minimum number of monitoring computers required to implement the methods used for fault detection and diagnosis in a distributed configuration subject to certain operational constraints. It also finds the sensors that need to be transmitted to each of the monitors. The proposed methods rely on concepts and algorithms from graph theory and optimization, and their efficiency is shown through a case study on the benchmark Tennessee Eastman Process.

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