Perspectives on Process Monitoring of Industrial Systems

Abstract Process monitoring systems are necessary for ensuring the long-term reliability of the operation of industrial systems. This article provides some perspectives on progress in the design of process monitoring systems over the last twenty years. Different techniques that can be applied for fault detection, fault identification, and fault diagnosis are summarized. The challenges in the field and opportunities for future research are discussed. When looking into the future, it is argued that advances are likely to come from combining different methods to exploit the strengths of various techniques while minimizing their weaknesses.

[1]  Richard D. Braatz,et al.  A Hybrid Stochastic-Deterministic Approach For Active Fault Diagnosis Using Scenario Optimization , 2014 .

[2]  Michel Verhaegen,et al.  Application of a subspace model identification technique to identify LTI systems operating in closed-loop , 1993, Autom..

[3]  S. Joe Qin,et al.  Reconstruction-based Contribution for Process Monitoring , 2008 .

[4]  J. Platt Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[5]  A. Arapostathis,et al.  Remarks on redundance in stability criteria and a counterexample to fullers conjecture , 1979 .

[6]  Michel Verhaegen,et al.  Identification of the deterministic part of MIMO state space models given in innovations form from input-output data , 1994, Autom..

[7]  Rolf Isermann,et al.  Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .

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

[9]  Theodora Kourti,et al.  The Process Analytical Technology initiative and multivariate process analysis, monitoring and control , 2006, Analytical and bioanalytical chemistry.

[10]  Walmir M. Caminhas,et al.  SVM practical industrial application for mechanical faults diagnostic , 2011, Expert Syst. Appl..

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

[12]  Dexian Huang,et al.  Canonical variate analysis-based monitoring of process correlation structure using causal feature representation , 2015 .

[13]  Sirish L. Shah,et al.  Fault detection and diagnosis in process data using one-class support vector machines , 2009 .

[14]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

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

[16]  R. Perrons,et al.  Assessing the damage caused by Deepwater Horizon: not just another Exxon Valdez. , 2013, Marine pollution bulletin.

[17]  H. Akaike Canonical Correlation Analysis of Time Series and the Use of an Information Criterion , 1976 .

[18]  Richard D. Braatz,et al.  Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis , 2000 .

[19]  Gábor Lugosi,et al.  Prediction, learning, and games , 2006 .

[20]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

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

[22]  Efstathios Bakolas,et al.  Texas City refinery accident: Case study in breakdown of defense-in-depth and violation of the safety–diagnosability principle in design , 2014 .

[23]  S.J. Qin,et al.  Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis , 2006, IEEE Transactions on Semiconductor Manufacturing.

[24]  Wallace E. Larimore,et al.  Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.

[25]  Richard D. Braatz,et al.  Diagnosis of multiple and unknown faults using the causal map and multivariate statistics , 2015 .

[26]  Richard D. Braatz,et al.  Active fault diagnosis using moving horizon input design , 2013, 2013 European Control Conference (ECC).

[27]  Ali Cinar,et al.  Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods , 2012, Comput. Chem. Eng..

[28]  Janet M. Baker,et al.  The Design for the Wall Street Journal-based CSR Corpus , 1992, HLT.

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

[30]  Ron J. Patton,et al.  FAULT-TOLERANT CONTROL SYSTEMS: THE 1997 SITUATION , 1997 .

[31]  P. Frank,et al.  Survey of robust residual generation and evaluation methods in observer-based fault detection systems , 1997 .

[32]  Rolf Isermann Model-based fault-detection and diagnosis - status and applications § , 2004 .

[33]  F John,et al.  Three mile island accident , 2008 .

[34]  Jin Jiang,et al.  Fault-tolerant control systems: A comparative study between active and passive approaches , 2012, Annu. Rev. Control..

[35]  Catherine Porte,et al.  Automation and optimization of glycine synthesis , 1996 .

[36]  I. Jolliffe Principal Component Analysis , 2002 .

[37]  Dale E Seborg,et al.  Fault Detection and Diagnosis in an Industrial Fed‐Batch Cell Culture Process , 2007, Biotechnology progress.

[38]  John F. MacGregor,et al.  Multivariate SPC charts for monitoring batch processes , 1995 .

[39]  Kristiaan Pelckmans,et al.  Towards an Online, Non-stochastic Approach to Fault Detection , 2014 .

[40]  S. Joe Qin,et al.  Analysis and generalization of fault diagnosis methods for process monitoring , 2011 .

[41]  R. K. Mehra,et al.  Correspondence item: An innovations approach to fault detection and diagnosis in dynamic systems , 1971 .

[42]  Dexian Huang,et al.  Canonical variate analysis-based contributions for fault identification , 2015 .

[43]  Leo H. Chiang,et al.  Process monitoring using causal map and multivariate statistics: fault detection and identification , 2003 .

[44]  A. Çinar,et al.  Online batch/fed-batch process performance monitoring, quality prediction, and variable-contribution analysis for diagnosis , 2003 .

[45]  Richard D. Braatz,et al.  A hybrid stochastic-deterministic input design method for active fault diagnosis , 2013, 52nd IEEE Conference on Decision and Control.

[46]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

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

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

[49]  Dale E. Seborg,et al.  Fault Detection Using Canonical Variate Analysis , 2004 .

[50]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[51]  Richard D. Braatz,et al.  Fault-tolerant model predictive control with active fault isolation , 2013, 2013 Conference on Control and Fault-Tolerant Systems (SysTol).

[52]  Richard D. Braatz,et al.  Input design for guaranteed fault diagnosis using zonotopes , 2014, Autom..

[53]  H. Wold Path Models with Latent Variables: The NIPALS Approach , 1975 .

[54]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[55]  Stefan Streif,et al.  Active Fault Diagnosis for Nonlinear Systems with Probabilistic Uncertainties , 2014 .

[56]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[57]  Silvio Simani,et al.  Model-based fault diagnosis in dynamic systems using identification techniques , 2003 .

[58]  Alan S. Willsky,et al.  A survey of design methods for failure detection in dynamic systems , 1976, Autom..

[59]  Ping Zhang,et al.  A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .

[60]  Jin Jiang,et al.  A stability guaranteed active fault-tolerant control system against actuator failures , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[61]  James Lam,et al.  An LMI approach to design robust fault detection filter for uncertain LTI systems , 2003, Autom..

[62]  Ramine Nikoukhah,et al.  Auxiliary Signal Design for Failure Detection , 2004 .

[63]  Dale E. Seborg,et al.  Identification of the Tennessee Eastman challenge process with subspace methods , 2000 .

[64]  Richard D. Braatz,et al.  A combined canonical variate analysis and Fisher discriminant analysis (CVA-FDA) approach for fault diagnosis , 2015, Comput. Chem. Eng..

[65]  Frank Pearson Lees,et al.  Loss prevention in the process industries : hazard identification, assessment, and control , 1980 .

[66]  Richard D. Braatz,et al.  Two-Dimensional Contribution Map for Fault Identification [Focus on Education] , 2014, IEEE Control Systems.

[67]  Dale Schuurmans,et al.  Unsupervised and Semi-Supervised Multi-Class Support Vector Machines , 2005, AAAI.

[68]  George Karypis,et al.  Mining bioprocess data: opportunities and challenges. , 2008, Trends in biotechnology.

[69]  Ramine Nikoukhah,et al.  Guaranteed Active Failure Detection and Isolation for Linear Dynamical Systems , 1998, Autom..

[70]  A. J. Morris,et al.  Statistical performance monitoring of dynamic multivariate processes using state space modelling , 2002 .

[71]  V. Verdult,et al.  Filtering and System Identification: Subspace model identification , 2007 .

[72]  Anurag S Rathore,et al.  Application of Multivariate Analysis toward Biotech Processes: Case Study of a Cell‐Culture Unit Operation , 2007, Biotechnology progress.

[73]  Inseok Hwang,et al.  A Survey of Fault Detection, Isolation, and Reconfiguration Methods , 2010, IEEE Transactions on Control Systems Technology.

[74]  Richard D. Braatz,et al.  Design of active inputs for set-based fault diagnosis , 2013, 2013 American Control Conference.