Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 22, Issue 12, Pages 2262-2271Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2011.2165853
Keywords
Data-based monitoring; dynamic total projection to latent structures; multivariate dynamic processes; quality-related monitoring
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Funding
- National 973 Project [2010CB731800, 2009CB32602]
- Natural Science Foundation of China [61020106003, 61021063, 61028010, 61074085]
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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.
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