Concurrent monitoring of global-local performance indicators for large-scale process

Abstract Multiblock methods with decision fusion are common schemes for performance indicator monitoring in large-scale process. However, single sub-block is insufficient for interpreting global performance indicator, thus the final fusion may cause inaccurate result. Besides, it is imperative to consider safety related local performance indicators (LPI) in each sub-block and important to model the correlation between each sub-block. In this paper, concurrent global-local performance indicator monitoring method is proposed. For more purposeful monitoring, this study constructs two feature subspaces, the local performance indicator related subspace (LPIRS) and the global performance indicator related subspace (GPIRS), with different significances. In LPIRS, LPI related variables in each sub-block are monitored. In GPIRS, considering the dynamic interactions between each sub-block, improved dynamic Canonical Correlation Analysis method is proposed for feature extraction. Moreover, the features are furtherly selected based on a novel selection criterion and the orthogonal decomposition on regression coefficients is employed to construct GPIRS. Finally, the effectiveness of the proposed method is validated via two cases.

[1]  Qiang Liu,et al.  Unevenly Sampled Dynamic Data Modeling and Monitoring With an Industrial Application , 2017, IEEE Transactions on Industrial Informatics.

[2]  Zhiqiang Ge,et al.  Review on data-driven modeling and monitoring for plant-wide industrial processes , 2017 .

[3]  Kaixiang Peng,et al.  A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches ☆ , 2015 .

[4]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[5]  Xuefeng Yan,et al.  Plant-wide process monitoring based on mutual information-multiblock principal component analysis. , 2014, ISA transactions.

[6]  Yang Wang,et al.  Data-Driven Distributed Local Fault Detection for Large-Scale Processes Based on the GA-Regularized Canonical Correlation Analysis , 2017, IEEE Transactions on Industrial Electronics.

[7]  Qiang Liu,et al.  Multiblock Concurrent PLS for Decentralized Monitoring of Continuous Annealing Processes , 2014, IEEE Transactions on Industrial Electronics.

[8]  Faisal Khan,et al.  A Bibliometric Review and Analysis of Data-Driven Fault Detection and Diagnosis Methods for Process Systems , 2018, Industrial & Engineering Chemistry Research.

[9]  Jian Yang,et al.  Performance monitoring method based on balanced partial least square and Statistics Pattern Analysis. , 2018, ISA transactions.

[10]  Xuefeng Yan,et al.  Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based multiblock KPCA , 2015 .

[11]  Plant-Wide Industrial Process Monitoring: A Distributed Modeling Framework , 2016, IEEE Transactions on Industrial Informatics.

[12]  Herman Wold,et al.  Soft modelling: The Basic Design and Some Extensions , 1982 .

[13]  Hongbo Shi,et al.  Fault Detection and Classification Using Quality-Supervised Double-Layer Method , 2018, IEEE Transactions on Industrial Electronics.

[14]  Fuli Wang,et al.  Multi-mode plant-wide process operating performance assessment based on a novel two-level multi-block hybrid model , 2018, Chemical Engineering Research and Design.

[15]  H. Shi,et al.  Parallel quality-related dynamic principal component regression method for chemical process monitoring , 2019, Journal of Process Control.

[16]  Kaixiang Peng,et al.  A novel dynamic non-Gaussian approach for quality-related fault diagnosis with application to the hot strip mill process , 2017, J. Frankl. Inst..

[17]  Dewei Li,et al.  Monitoring big process data of industrial plants with multiple operating modes based on Hadoop , 2018, Journal of the Taiwan Institute of Chemical Engineers.

[18]  John F. MacGregor,et al.  Process monitoring and diagnosis by multiblock PLS methods , 1994 .

[19]  Zhiqiang Ge,et al.  Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes With Big Data , 2017, IEEE Transactions on Industrial Informatics.

[20]  Kaixiang Peng,et al.  A KPI-based process monitoring and fault detection framework for large-scale processes. , 2017, ISA transactions.

[21]  S. Joe Qin,et al.  Quality‐relevant and process‐relevant fault monitoring with concurrent projection to latent structures , 2013 .

[22]  Donghua Zhou,et al.  Total projection to latent structures for process monitoring , 2009 .

[23]  Okyay Kaynak,et al.  Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[24]  Zhiqiang Ge,et al.  Distributed predictive modeling framework for prediction and diagnosis of key performance index in plant-wide processes , 2017 .

[25]  Guang Wang,et al.  Quality-Related Fault Detection Approach Based on Orthogonal Signal Correction and Modified PLS , 2015, IEEE Transactions on Industrial Informatics.

[26]  Fuli Wang,et al.  Comprehensive economic index prediction based operating optimality assessment and nonoptimal cause identification for multimode processes , 2015 .

[27]  Zhiqiang Ge,et al.  Large-scale plant-wide process modeling and hierarchical monitoring: A distributed Bayesian network approach , 2017 .

[28]  Jian Yang,et al.  Dynamic learning on the manifold with constrained time information and its application for dynamic process monitoring , 2017 .

[29]  Fuli Wang,et al.  Operating optimality assessment and nonoptimal cause identification for non-Gaussian multimode processes with transitions , 2015 .

[30]  Donghua Zhou,et al.  Geometric properties of partial least squares for process monitoring , 2010, Autom..

[31]  Yang Tao,et al.  Performance-Indicator-Oriented Concurrent Subspace Process Monitoring Method , 2019, IEEE Transactions on Industrial Electronics.

[32]  Hongbo Shi,et al.  Temporal-Spatial Global Locality Projections for Multimode Process Monitoring , 2018, IEEE Access.

[33]  Hao Luo,et al.  Quality-related fault detection using linear and nonlinear principal component regression , 2016, J. Frankl. Inst..