Linearity Evaluation and Variable Subset Partition Based Hierarchical Process Modeling and Monitoring

Complex industrial processes may be formulated with hybrid correlations, indicating that linear and nonlinear relationships simultaneously exist among process variables, which brings great challenges for process monitoring. However, previous work did not consider the hybrid correlations and treated...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2018-03, Vol.65 (3), p.2683-2692
Main Authors: Li, Wenqing, Zhao, Chunhui, Gao, Furong
Format: Article
Language:English
Subjects:
Correlation
Covariance matrices
Fault detection
Feature extraction
Hierarchical modeling and monitoring
Industrial applications
Iterative methods
Kernel
linear correlations and nonlinear correlations
Linearity
Mathematical models
Model accuracy
Monitoring
Nonlinear analysis
Partitions
Principal component analysis
process monitoring
Process variables
variable separation
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Summary:Complex industrial processes may be formulated with hybrid correlations, indicating that linear and nonlinear relationships simultaneously exist among process variables, which brings great challenges for process monitoring. However, previous work did not consider the hybrid correlations and treated all the variables as a single subject in which single linear or nonlinear analysis method was employed based on prior process knowledge or some evaluation results, which may degrade the model accuracy and monitoring performance. Therefore, for complex processes with hybrid correlations, this paper proposes a linearity evaluation and variable subset partition based hierarchical modeling and monitoring method. First, linear variable subsets are separated from nonlinear subsets through an iterative variable correlation evaluation procedure. Second, hierarchical models are developed to capture linear patterns and nonlinear patterns in different levels. Third, a hierarchical monitoring strategy is proposed to monitor linear feature and nonlinear feature separately. By separating and modeling different types of variable correlations, the proposed method can explore more accurate process characteristics and thus improve the fault detection ability. Numerical examples and industrial applications are presented to illustrate its efficiency.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2017.2745452