Recursive-CPLS-Based Quality-Relevant and Process-Relevant Fault Monitoring With Application to the Tennessee Eastman Process

In industrial processes, the quality of the product is crucial. The batch partial least squares (PLS) monitoring model can effectively monitor for quality-related faults. In process monitoring, to overcome time-varying disturbances, the monitoring model needs to be updated regularly. Efficiently updating the monitoring model represents a serious problem. This paper proposes a recursive concurrent projection to latent structures (RCPLS) algorithm, which can both update models more efficiently with historical model parameters and new data and provide better quality-related fault monitoring results than can static concurrent projection to latent structures (CPLS). Based on RCPLS, a complete set of process monitoring technologies is proposed. These technologies can automatically filter and store modellable data and adaptively update the online monitoring model. The updated computational quantities of the RCPLS model and the CPLS model are compared through the Tennessee Eastman process (TEP). The effectiveness of the RCPLS algorithm is verified, and a comprehensive comparison of the quality-related fault detection capabilities of RCPLS and CPLS is performed. The results show that RCPLS can significantly reduce the computational burden and increase the monitoring performance.

[1]  Huijun Gao,et al.  Data-Based Techniques Focused on Modern Industry: An Overview , 2015, IEEE Transactions on Industrial Electronics.

[2]  Q. Jin,et al.  Quality-Related Statistical Process Monitoring Method Based on Global and Local Partial Least-Squares Projection , 2016 .

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

[4]  Steven X. Ding,et al.  Study on modifications of PLS approach for process monitoring , 2011 .

[5]  Bhupinder S. Dayal,et al.  Recursive exponentially weighted PLS and its applications to adaptive control and prediction , 1997 .

[6]  Zhiqiang Ge,et al.  Process Data Analytics via Probabilistic Latent Variable Models: A Tutorial Review , 2018, Industrial & Engineering Chemistry Research.

[7]  Kaixiang Peng,et al.  Adaptive total PLS based quality-relevant process monitoring with application to the Tennessee Eastman process , 2015, Neurocomputing.

[8]  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..

[9]  S. Qin.  Recursive PLS algorithms for adaptive data modeling , 1998 .

[10]  I. Jolliffe.  Principal Component Analysis and Factor Analysis , 1986 .

[11]  Jing Wang,et al.  A Quality-Related Statistical Process Monitoring Method Based on Global plus Local Projection to Latent Structures , 2018 .

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

[13]  Kaixiang Peng,et al.  Implementing multivariate statistics-based process monitoring: A comparison of basic data modeling approaches , 2018, Neurocomputing.

[14]  Steven X. Ding,et al.  A Review on Basic Data-Driven Approaches for Industrial Process Monitoring , 2014, IEEE Transactions on Industrial Electronics.

[15]  Zhiqiang Ge,et al.  Data Mining and Analytics in the Process Industry: The Role of Machine Learning , 2017, IEEE Access.

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

[17]  Si-Zhao Joe Qin,et al.  Dynamic latent variable analytics for process operations and control , 2017, Comput. Chem. Eng..

[18]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[19]  Kaixiang Peng,et al.  Quality-relevant fault monitoring based on efficient projection to latent structures with application to hot strip mill process , 2015 .

[20]  K. Helland,et al.  Recursive algorithm for partial least squares regression , 1992 .

[21]  S. Wold.  Exponentially weighted moving principal components analysis and projections to latent structures , 1994 .

[22]  Kaixiang Peng,et al.  Quality-related process monitoring for dynamic non-Gaussian batch process with multi-phase using a new data-driven method , 2016, Neurocomputing.

[23]  Furong Gao,et al.  Review of Recent Research on Data-Based Process Monitoring , 2013 .

[24]  Torsten Jeinsch,et al.  Quality-Related Fault Detection in Industrial Multimode Dynamic Processes , 2014, IEEE Transactions on Industrial Electronics.

[25]  Leo H. Chiang,et al.  Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis , 2000 .

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

[27]  Jing Wang,et al.  Quality-Relevant Fault Monitoring Based on Locality-Preserving Partial Least-Squares Statistical Models , 2017 .

[28]  Si-Zhao Joe Qin,et al.  Survey on data-driven industrial process monitoring and diagnosis , 2012, Annu. Rev. Control..

[29]  Zhiqiang Ge,et al.  Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data , 2018, Annu. Rev. Control..