Dynamic decentralized monitoring for large-scale industrial processes using multiblock canonical variate analysis based regression

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Fuente, M.J., Sainz-Palmero, G.I., Galende-Hernandez, M.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayes methods; Bayesian analysis; Bayesian inference; Canonical Variate Analysis; Correlation; Decentralized process monitoring; Decision making; Dynamic characteristics; Fault detection; Industrial plants; Monitoring; Multilayer perceptrons; Principal component analysis; Process monitoring; Process variables; Regression; Robustness (mathematics); Statistical analysis; Statistical inference
• DOI: 10.1109/ACCESS.2023.3256719
• EISSN: 2169-3536

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.