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Review of Recent Research on Data-Based Process Monitoring
Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic.Expand
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Process Monitoring Based on Independent Component Analysis - Principal Component Analysis ( ICA - PCA ) and Similarity Factors
Many of the current multivariate statistical process monitoring techniques (such as principal component analysis (PCA) or partial least squares (PLS)) do not utilize the non-Gaussian information ofExpand
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Data Mining and Analytics in the Process Industry: The Role of Machine Learning
TLDR
Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. Expand
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A comparative study of just-in-time-learning based methods for online soft sensor modeling
TLDR
A comparative study of three different just-in-time learning (JITL) methods for online soft sensor modeling is carried out, which are based on partial least squares (PLS), support vector regression (SVR) and least squares support Vector regression (LSSVR). Expand
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Distributed PCA Model for Plant-Wide Process Monitoring
For plant-wide process monitoring, most traditional multiblock methods are under the assumption that some process knowledge should be incorporated for dividing the process into several sub-blocks.Expand
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Online monitoring of nonlinear multiple mode processes based on adaptive local model approach
Abstract A new adaptive local model based monitoring approach is proposed for online monitoring of nonlinear multiple mode processes with non-Gaussian information. To solve the multiple mode problem,Expand
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Global–Local Structure Analysis Model and Its Application for Fault Detection and Identification
In this paper, a new fault detection and identification scheme that is based on the global–local structure analysis (GLSA) model is proposed. By exploiting the underlying geometrical manifold andExpand
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Improved kernel PCA-based monitoring approach for nonlinear processes
Abstract Conventional kernel principal component analysis (KPCA) may not function well for nonlinear processes, since the Gaussian assumption of the method may be violated through nonlinear andExpand
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Mixture Bayesian regularization method of PPCA for multimode process monitoring
This article intends to address two drawbacks of the traditional principal component analysis (PCA)-based monitoring method: (1) nonprobabilistic; (2) single operation mode assumption. On the basisExpand
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Nonlinear process monitoring based on linear subspace and Bayesian inference
Abstract This paper proposes a novel linear subspace and Bayesian inference based monitoring method for nonlinear processes. Through the introduced linear subspace method, the original nonlinearExpand
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