Fault Detection and Diagnosis for Nonlinear and Non-Gaussian Processes Based on Copula Subspace Division

A novel copula subspace division strategy is proposed for fault detection and diagnosis. High-dimensional industrial data are analyzed in two elemental subspaces: margin distribution subspace (MDS) modeled by joint margin distribution, and dependence structure subspace (DSS) modeled by copula. The highest density regions of two submodels are introduced and quantified using probability indices. To improve the robustness of the monitoring index, a hyperrectangular control boundary in MDS is designed, and the equivalent univariate control limits are estimated. Two associated contribution indices are also constructed for fault diagnosis. The interactive relationships among the root-cause variables are investigated via a proposed state chart. The effectiveness and superiority of the proposed approaches (double-subspace and multisubspace) are validated using a numerical example and the Tennessee Eastman chemical process. Better monitoring performance is achieved compared with some conventional approaches such a...

[1]  In-Beum Lee,et al.  Fault Detection of Non-Linear Processes Using Kernel Independent Component Analysis , 2008 .

[2]  Masoud Soroush,et al.  Dynamic risk analysis using alarm databases to improve process safety and product quality: Part II—Bayesian analysis , 2012 .

[3]  Vikram Garaniya,et al.  A probabilistic multivariate method for fault diagnosis of industrial processes , 2015 .

[4]  W. Melssen,et al.  Multivariate statistical process control using mixture modelling , 2005 .

[5]  Hongwei Tong,et al.  Detection of gross erros in data reconciliation by principal component analysis , 1995 .

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

[7]  Rebuttal to the ‘Comment on “Rolling Pin Method: Efficient General Method of Joint Probability Modeling”’ , 2015 .

[8]  Faisal Khan,et al.  Risk-based operational performance analysis using loss functions , 2014 .

[9]  Jialin Liu,et al.  Fault isolation using modified contribution plots , 2014, Comput. Chem. Eng..

[10]  Jian Yang,et al.  Kernel ICA: An alternative formulation and its application to face recognition , 2005, Pattern Recognit..

[11]  Faisal Khan,et al.  Development of Economic Consequence Methodology for Process Risk Analysis , 2015, Risk analysis : an official publication of the Society for Risk Analysis.

[12]  A. Frigessi,et al.  Pair-copula constructions of multiple dependence , 2009 .

[13]  Chunhui Zhao,et al.  A sparse dissimilarity analysis algorithm for incipient fault isolation with no priori fault information , 2017 .

[14]  Hongyang Yu.  Dynamic risk assessment of complex process operations based on a novel synthesis of soft-sensing and loss function , 2017 .

[15]  Donghua Zhou,et al.  Generalized Reconstruction-Based Contributions for Output-Relevant Fault Diagnosis With Application to the Tennessee Eastman Process , 2011, IEEE Transactions on Control Systems Technology.

[16]  Theodora Kourti,et al.  Process analysis, monitoring and diagnosis, using multivariate projection methods , 1995 .

[17]  Rob J Hyndman,et al.  Computing and Graphing Highest Density Regions , 1996 .

[18]  Chenglin Wen,et al.  Fault Isolation Based on k-Nearest Neighbor Rule for Industrial Processes , 2016, IEEE Transactions on Industrial Electronics.

[19]  Chunhui Zhao,et al.  A Sparse Reconstruction Strategy for Online Fault Diagnosis in Nonstationary Processes with No a Priori Fault Information , 2017 .

[20]  Shaojun Li,et al.  Vine Copula-Based Dependence Description for Multivariate Multimode Process Monitoring , 2015 .

[21]  Nima Khakzad,et al.  Dynamic risk analysis using bow-tie approach , 2012, Reliab. Eng. Syst. Saf..

[22]  C. Yoo,et al.  Nonlinear process monitoring using kernel principal component analysis , 2004 .

[23]  Wenjun Huang,et al.  A distributed parallel alarm management strategy for alarm reduction in chemical plants , 2015 .

[24]  Warren D. Seider,et al.  Plant-specific dynamic failure assessment using Bayesian theory , 2006 .

[25]  Claudia Czado,et al.  Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas , 2015, J. Multivar. Anal..

[26]  Masoud Soroush,et al.  Rolling Pin Method: Efficient General Method of Joint Probability Modeling , 2014 .

[27]  ChangKyoo Yoo,et al.  Statistical process monitoring with independent component analysis , 2004 .

[28]  S. Joe Qin,et al.  Reconstruction-Based Fault Identification Using a Combined Index , 2001 .

[29]  Xuefeng Yan,et al.  Chemical processes monitoring based on weighted principal component analysis and its application , 2012 .

[30]  Guangyu Mao,et al.  Testing independence in high dimensions using Kendall's tau , 2018, Comput. Stat. Data Anal..

[31]  Shaojun Li,et al.  Sequential Dependence Modeling Using Bayesian Theory and D-Vine Copula and Its Application on Chemical Process Risk Prediction , 2014 .

[32]  Jin Hyun Park,et al.  Fault detection and identification of nonlinear processes based on kernel PCA , 2005 .

[33]  Iman Izadi,et al.  Pattern matching of alarm flood sequences by a modified Smith–Waterman algorithm , 2013 .

[34]  P. Grosdidier,et al.  A Path Forward for DCS Alarm Management , 2003 .

[35]  A. Bowman.  An alternative method of cross-validation for the smoothing of density estimates , 1984 .

[36]  Manabu Kano,et al.  Statistical process monitoring based on dissimilarity of process data , 2002 .

[37]  Faisal Khan,et al.  Dynamic risk assessment using failure assessment and Bayesian theory , 2009 .

[38]  Vikram Garaniya,et al.  A sparse PCA for nonlinear fault diagnosis and robust feature discovery of industrial processes , 2016 .

[39]  Faisal Khan,et al.  Application of loss functions in process economic risk assessment , 2016 .

[40]  Rajagopalan Srinivasan,et al.  Quantifying the effectiveness of an alarm management system through human factors studies , 2014, Comput. Chem. Eng..

[41]  Jie Yu,et al.  Localized Fisher discriminant analysis based complex chemical process monitoring , 2011 .

[42]  Jian Hou,et al.  Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes , 2016, Neurocomputing.

[43]  Claudia Czado,et al.  Selection of sparse vine copulas in high dimensions with the Lasso , 2019, Stat. Comput..

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