RT info:eu-repo/semantics/article
T1 Dynamic decentralized monitoring for large-scale industrial processes using multiblock canonical variate analysis based regression
A1 Fuente Aparicio, María Jesús de la
A1 Sáinz Palmero, Gregorio Ismael
A1 Galende Hernández, Marta
K1 Fault detection
K1 Canonical variate analysis
K1 Regression
K1 Decentralized process monitoring
K1 Bayesian inference
AB Decentralized monitoring methods, which divide the process variables into several blocks and perform local monitoring for each sub-block, have been gaining increasing attention in large-scale plant-wide monitoring due to the complexity of their processes. In such methods, the dynamic nature of the process data is a relevant issue which is not usually managed. Here, a new data-driven distributed dynamic monitoring scheme is proposed to deal with this issue, integrating regression to automatically divide the blocks, a multivariate and dynamic statistical analysis (Canonical Variate Analysis, CVA) to perform local monitoring, and Bayesian inference to achieve the decision making. By constructing sub-blocks using regression, it is possible to identify the most commonly associated variables for every block. Three regression methods are proposed: LASSO (Least Absolute Shrinkage and Selection Operator), which forces the coefficients of the less relevant variables towards zero; Elastic-net, a robust method that is a compromise between Ridge and Lasso regression; and, finally, a non-linear regression method based on the Multilayer Perceptron Network (MLP). Then, the CVA model is implemented for each sub-block to consider the dynamic characteristics of the industrial processes and the Bayesian inference provides a global decision for fault detection. The Tennessee Eastman benchmark validates the efficiency and feasibility of the proposed method regarding some state-of-the-art methods.
PB IEEE
SN 2169-3536
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/75068
UL https://uvadoc.uva.es/handle/10324/75068
LA eng
NO IEEE Access, 11, 26611-26623
NO Producción Científica
DS UVaDOC
RD 04-mar-2025