Data-Driven Optimized Distributed Dynamic PCA for Efficient Monitoring of Large-Scale Dynamic Processes

Dynamic principal component analysis (DPCA) is generally employed in monitoring dynamic processes and typically incorporates all measured variables. However, for a large-scale process, the inclusion of variables without fault-relevant information may cause redundancy and degrade monitoring performance. In this paper, the influence of variable and time-lagged variable selection on the DPCA monitoring performance is analyzed. Then, a fault-relevant performance-driven distributed monitoring scheme is proposed to achieve efficient fault detection and diagnosis. First, performance-driven process decomposition is performed, and the optimal subset of variables and time-lagged variables for each fault are selected through a stochastic optimization algorithm. Second, local DPCA models are established to characterize the process dynamics and generate fault signature evidence. Finally, a Bayesian diagnosis system with the most efficient evidence sources is established to identify the process status. Case studies on a numerical example and the Tennessee Eastman benchmark process demonstrate the efficiency of the proposed monitoring scheme.

[1]  Biao Huang.  Bayesian methods for control loop monitoring and diagnosis , 2008 .

[2]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[3]  Donghua Zhou,et al.  Quality Relevant Data-Driven Modeling and Monitoring of Multivariate Dynamic Processes: The Dynamic T-PLS Approach , 2011, IEEE Transactions on Neural Networks.

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

[5]  Christos Georgakis,et al.  Disturbance detection and isolation by dynamic principal component analysis , 1995 .

[6]  J. Macgregor,et al.  Analysis of multiblock and hierarchical PCA and PLS models , 1998 .

[7]  Biao Huang,et al.  Bayesian methods for control loop diagnosis in the presence of temporal dependent evidences , 2011, Autom..

[8]  Biao Huang,et al.  GMM and optimal principal components-based Bayesian method for multimode fault diagnosis , 2016, Comput. Chem. Eng..

[9]  Biao Huang,et al.  Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference , 2016, IEEE Transactions on Industrial Electronics.

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

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

[12]  Hong Zhou,et al.  Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Partial Least Squares , 2010, IEEE Transactions on Industrial Informatics.

[13]  A. Negiz,et al.  Statistical monitoring of multivariable dynamic processes with state-space models , 1997 .

[14]  Xuefeng Yan,et al.  Just‐in‐time reorganized PCA integrated with SVDD for chemical process monitoring , 2014 .

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

[16]  Michael J. Piovoso,et al.  On unifying multiblock analysis with application to decentralized process monitoring , 2001 .

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

[18]  Junghui Chen,et al.  On-line batch process monitoring using dynamic PCA and dynamic PLS models , 2002 .

[19]  Kaushik Ghosh,et al.  Optimal variable selection for effective statistical process monitoring , 2014, Comput. Chem. Eng..

[20]  Anna Pernestål,et al.  Probabilistic Fault Diagnosis with Automotive Applications , 2009 .

[21]  S. Joe Qin,et al.  Statistical process monitoring: basics and beyond , 2003 .

[22]  Steven X. Ding,et al.  Data-driven design of monitoring and diagnosis systems for dynamic processes: A review of subspace technique based schemes and some recent results , 2014 .

[23]  Yingying Ren,et al.  Fault Detection for Two-Dimensional Roesser Systems With Sensor Faults , 2016, IEEE Access.

[24]  In-Beum Lee,et al.  Multiblock PLS-based localized process diagnosis , 2005 .

[25]  Zhiqiang Ge,et al.  Performance-driven ensemble learning ICA model for improved non-Gaussian process monitoring , 2013 .

[26]  Zhi-huan Song,et al.  Distributed PCA Model for Plant-Wide Process Monitoring , 2013 .