FRDPC subspace construction integrated with Bayesian inference for efficient monitoring of dynamic chemical processes

Modern chemical processes are usually characterized by large-scale, complex correlation, and strong dynamics, and monitoring of such processes is imperative. This paper proposes a performance-driven fault-relevant dynamic principal component (FRDPC) subspace construction integrated with Bayesian inference method to achieve efficient monitoring for dynamic chemical processes. First, dynamic principal component analysis is employed to deal with both auto-correlation and cross-correlation among variables. Second, considering fault information has no definite mapping to a certain dynamic principal component (DPC) and the existence of non-beneficial DPCs may cause redundancy in the monitoring, an FRDPC subspace is constructed for each fault through the performance-driven DPC selection. Then new process measurements are examined in each FRDPC subspace as well as the residual subspace. The monitoring results in all subspaces are fused to a comprehensive index through Bayesian inference to provide an intuitive indication of the process status. Case studies on a numerical example and the Tennessee Eastman benchmark process indicate the efficiency.

[1]  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.

[2]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

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

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

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

[6]  Steven X. Ding,et al.  Real-Time Implementation of Fault-Tolerant Control Systems With Performance Optimization , 2014, IEEE Transactions on Industrial Electronics.

[7]  Steven X. Ding,et al.  Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems , 2014 .

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

[9]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[10]  Juan Li,et al.  Performance-driven optimal design of distributed monitoring for large-scale nonlinear processes , 2016 .

[11]  B. K. Ghosh,et al.  Some Monotonicity Theorems for χ2, F and T Distributions with Applications , 1973 .

[12]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[13]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

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

[15]  S. Joe Qin,et al.  Determining the number of principal components for best reconstruction , 1998 .

[16]  Zhiqiang Ge,et al.  Nonlinear process monitoring based on linear subspace and Bayesian inference , 2010 .

[17]  Chunhui Zhao,et al.  Fault-relevant Principal Component Analysis (FPCA) method for multivariate statistical modeling and process monitoring , 2014 .

[18]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[19]  Theodora Kourti,et al.  Multivariate SPC Methods for Process and Product Monitoring , 1996 .

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

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

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

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

[24]  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.

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

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