Data-driven monitoring for stochastic systems and its application on batch process

Batch processes are characterised by a prescribed processing of raw materials into final products for a finite duration and play an important role in many industrial sectors due to the low-volume and high-value products. Process dynamics and stochastic disturbances are inherent characteristics of batch processes, which cause monitoring of batch processes a challenging problem in practice. To solve this problem, a subspace-aided data-driven approach is presented in this article for batch process monitoring. The advantages of the proposed approach lie in its simple form and its abilities to deal with stochastic disturbances and process dynamics existing in the process. The kernel density estimation, which serves as a non-parametric way of estimating the probability density function, is utilised for threshold calculation. An industrial benchmark of fed-batch penicillin production is finally utilised to verify the effectiveness of the proposed approach.

[1]  Zidong Wang,et al.  Bounded $H_{\infty}$ Synchronization and State Estimation for Discrete Time-Varying Stochastic Complex Networks Over a Finite Horizon , 2011, IEEE Transactions on Neural Networks.

[2]  Tao Chen,et al.  Multivariate statistical monitoring of two-dimensional dynamic batch processes utilizing non-Gaussian information , 2010 .

[3]  Zidong Wang,et al.  A Stochastic Sampled-Data Approach to Distributed $H_{\infty }$ Filtering in Sensor Networks , 2011, IEEE Transactions on Circuits and Systems I: Regular Papers.

[4]  John F. MacGregor,et al.  Multi-way partial least squares in monitoring batch processes , 1995 .

[5]  Gülnur Birol,et al.  A modular simulation package for fed-batch fermentation: penicillin production , 2002 .

[6]  Steven X. Ding,et al.  Recursive identification algorithms to design fault detection systems , 2010 .

[7]  Steven X. Ding,et al.  A projection-based method of fault detection for linear discrete time-varying systems , 2013, Int. J. Syst. Sci..

[8]  Youmin Zhang,et al.  Design of a fault tolerant control system incorporating reliability analysis and dynamic behaviour constraints , 2011, Int. J. Syst. Sci..

[9]  Paulo J. Lopes dos Santos,et al.  A new insight to the matrices extraction in a MOESP type subspace identification algorithm , 2006, Int. J. Syst. Sci..

[10]  Rolf Isermann,et al.  Fault-diagnosis systems : an introduction from fault detection to fault tolerance , 2006 .

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

[12]  Dirk P. Kroese,et al.  Kernel density estimation via diffusion , 2010, 1011.2602.

[13]  T. McAvoy,et al.  Batch tracking via nonlinear principal component analysis , 1996 .

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

[15]  ChangKyoo Yoo,et al.  On-line batch process monitoring using a consecutively updated multiway principal component analysis model , 2003, Comput. Chem. Eng..

[16]  Zidong Wang,et al.  On Nonlinear $H_{\infty }$ Filtering for Discrete-Time Stochastic Systems With Missing Measurements , 2008, IEEE Transactions on Automatic Control.

[17]  Paul M. Frank,et al.  Issues of Fault Diagnosis for Dynamic Systems , 2010, Springer London.

[18]  J. Macgregor,et al.  Monitoring batch processes using multiway principal component analysis , 1994 .

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

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

[21]  U. Kruger,et al.  Dynamic Principal Component Analysis Using Subspace Model Identification , 2005, ICIC.

[22]  Michel Kinnaert,et al.  Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger , 2011, Int. J. Syst. Sci..

[23]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

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

[25]  Fuli Wang,et al.  On-line batch process monitoring using batch dynamic kernel principal component analysis , 2010 .

[26]  Yeung Sam Hung,et al.  Distributed $H_{\infty}$ Filtering for Polynomial Nonlinear Stochastic Systems in Sensor Networks , 2011, IEEE Transactions on Industrial Electronics.

[27]  A. J. Morris,et al.  Non-parametric confidence bounds for process performance monitoring charts☆ , 1996 .

[28]  B. Moor,et al.  Subspace state space system identification for industrial processes , 1998 .

[29]  ChangKyoo Yoo,et al.  Fault detection of batch processes using multiway kernel principal component analysis , 2004, Comput. Chem. Eng..

[30]  Si-Zhao Joe Qin,et al.  An overview of subspace identification , 2006, Comput. Chem. Eng..

[31]  Silvio Simani,et al.  Model-Based Fault Diagnosis Techniques , 2003 .

[32]  S. Ding,et al.  SUBSPACE METHOD AIDED DATA-DRIVEN DESIGN OF OBSERVER BASED FAULT DETECTION SYSTEMS , 2005 .

[33]  Ping Zhang,et al.  On fault detection in linear discrete-time, periodic, and sampled-data systems , 2008 .

[34]  Ping Zhang,et al.  Subspace method aided data-driven design of fault detection and isolation systems , 2009 .

[35]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[36]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[37]  Lamiaa M. Elshenawy,et al.  Efficient Recursive Principal Component Analysis Algorithms for Process Monitoring , 2010 .

[38]  Bernard W. Silverman,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

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

[40]  B. Moor,et al.  Subspace identification for linear systems , 1996 .

[41]  Torsten Jeinsch,et al.  A Survey of the Application of Basic Data-Driven and Model-Based Methods in Process Monitoring and Fault Diagnosis , 2011 .

[42]  ChangKyoo Yoo,et al.  On-line monitoring of batch processes using multiway independent component analysis , 2004 .

[43]  Michel Kinnaert,et al.  Diagnosis and Fault-Tolerant Control , 2004, IEEE Transactions on Automatic Control.

[44]  John F. MacGregor,et al.  Adaptive batch monitoring using hierarchical PCA , 1998 .

[45]  Zidong Wang,et al.  Quantized $H_{\infty }$ Control for Nonlinear Stochastic Time-Delay Systems With Missing Measurements , 2012, IEEE Transactions on Automatic Control.