Fault propagation path estimation in NGL fractionation process using principal component analysis

Abstract Multivariate statistical methods for process monitoring are attaining a lot of attention in chemical and process industries to enhance both the process performance and safety. The fault in one process variable readily affects the other variables which makes it difficult to identify the fault variable precisely. In this study, principal component analysis (PCA) model has been developed and applied to monitor the NGL (natural gas liquid) fractionation process. Normal and fault case scenarios are developed and compared statistically to identify the fault variable and to estimate the fault propagation path in the system. The simulated NGL plant is first validated against the design data and then the developed methodology is applied to predict the fault direction by projecting the samples on the residual subspace (RS). The RS of fault data is usually superimposed by normal variations which must be eliminated to amplify the fault magnitude. The RS is further transformed into co-variance matrix followed by Singular Value Decomposition (SVD) analysis to generate the fault direction matrix corresponding to the highest eigenvalue. The process variables are further analyzed according to their magnitude of contribution towards a particular fault that in turn can be used for the determination of fault propagation path in the system. Furthermore, the applied methodology can quickly detect the fault variable irrespective of using the fault detection indices where the variable showing highest variation is most likely to be the fault variable.

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

[2]  Jie Zhang,et al.  Fault Localization in Batch Processes through Progressive Principal Component Analysis Modeling , 2011 .

[3]  S. J. Qin,et al.  Extracting fault subspaces for fault identification of a polyester film process , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[4]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .

[5]  Michael S. Dudzic,et al.  An industrial perspective on implementing on-line applications of multivariate statistics , 2004 .

[6]  Thomas F. Edgar,et al.  Identification of faulty sensors using principal component analysis , 1996 .

[7]  Ali Cinar,et al.  Statistical process monitoring and disturbance diagnosis in multivariable continuous processes , 1996 .

[8]  Alberto Ferrer,et al.  Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process , 2007 .

[9]  Nicholas P. Cheremisinoff,et al.  Handbook of Chemical Processing Equipment , 1995 .

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

[11]  Luis Eduardo Mujica,et al.  Q-statistic and T2-statistic PCA-based measures for damage assessment in structures , 2011 .

[12]  Karlene A. Kosanovich,et al.  Improved Process Understanding Using Multiway Principal Component Analysis , 1996 .

[13]  Thomas E. Marlin,et al.  Multivariate statistical monitoring of process operating performance , 1991 .

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

[15]  Jianchang Liu,et al.  Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy , 2016 .

[16]  Stella Bezergianni,et al.  Application of Principal Component Analysis for Monitoring and Disturbance Detection of a Hydrotreating Process , 2008 .

[17]  Seongkyu Yoon,et al.  Principal‐component analysis of multiscale data for process monitoring and fault diagnosis , 2004 .

[18]  J. M. Vinson,et al.  Statistical approaches to fault analysis in multivariate process control , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[19]  Xue-feng Yan,et al.  Fault Detection and Diagnosis in Chemical Processes Using Sensitive Principal Component Analysis , 2013 .

[20]  H. Yue,et al.  Fault detection of plasma etchers using optical emission spectra , 2000 .

[21]  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.

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

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

[24]  Sirkka-Liisa Jämsä-Jounela,et al.  Hybrid approach to casual analysis on a complex industrial system based on transfer entropy in conjunction with process connectivity information , 2016 .

[25]  Julian Morris,et al.  Progressive multi-block modelling for enhanced fault isolation in batch processes , 2014 .