Quality Relevant Data-Driven Modeling and Monitoring of Multivariate Dynamic Processes: The Dynamic T-PLS Approach
- 14 November 2011
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 22 (12), 2262-2271
- https://doi.org/10.1109/tnn.2011.2165853
Abstract
In data-based monitoring field, the nonlinear iterative partial least squares procedure has been a useful tool for process data modeling, which is also the foundation of projection to latent structures (PLS) models. To describe the dynamic processes properly, a dynamic PLS algorithm is proposed in this paper for dynamic process modeling, which captures the dynamic correlation between the measurement block and quality data block. For the purpose of process monitoring, a dynamic total PLS (T-PLS) model is presented to decompose the measurement block into four subspaces. The new model is the dynamic extension of the T-PLS model, which is efficient for detecting quality-related abnormal situation. Several examples are given to show the effectiveness of dynamic T-PLS models and the corresponding fault detection methods.Keywords
This publication has 22 references indexed in Scilit:
- Local dynamic partial least squares approaches for the modelling of batch processesThe Canadian Journal of Chemical Engineering, 2008
- Multiple-Fault Diagnosis of the Tennessee Eastman Process Based on System Decomposition and Dynamic PLSIndustrial & Engineering Chemistry Research, 2004
- An online application of dynamic PLS to a dearomatization processComputers & Chemical Engineering, 2004
- Multiple-Fault Diagnosis Based on System Decomposition and Dynamic PLSIndustrial & Engineering Chemistry Research, 2003
- Non-Linear Model Based Predictive Control Through Dynamic Non-Linear Partial Least SquaresChemical Engineering Research and Design, 2002
- On-line batch process monitoring using dynamic PCA and dynamic PLS modelsChemical Engineering Science, 2001
- Non-linear dynamic projection to latent structures modellingChemometrics and Intelligent Laboratory Systems, 2000
- Inferential control system of distillation compositions using dynamic partial least squares regressionJournal of Process Control, 2000
- Nonlinear FIR modeling via a neural net PLS approachComputers & Chemical Engineering, 1996
- Multivariate statistical monitoring of process operating performanceThe Canadian Journal of Chemical Engineering, 1991