Process monitoring using non-linear statistical techniques

Abstract A large number of process variables are usually measured and stored in computer data base during process operation. These variables are usually highly correlated and the real dimensionality of the monitored process is considerably less than that represented by the number of process variables collected. Successful process performance monitoring requires the efficient handling of large amounts of monitored plant data. Principal component analysis reduces the dimensionality of the process by creating a new set of variables, principal components, which attempt to reflect the true underlying system dimension. Process performance can then be monitored in a low dimensional principal component space. Linear process performance monitoring is based upon plots of scores and squared prediction errors from a principal component model. However, for highly non-linear processes, this form of monitoring may not be efficient since the process dimensionality cannot be represented by a small number of linear principal components. Non-linearly correlated process variables can be reduced to a set of non-linear principal components, through the application of non-linear principal component analysis. Efficient process monitoring can then be performed in a low dimensional non-linear principal component space. In parallel with the conventional multivariate plots, the use of accumulated scores provides a significant breakthrough in the separation of different operating conditions/faults, leading to robust early warning of potential plant malfunctions. An application to the condition monitoring of a polymerisation reactor demonstrates the effectiveness of the non-linear monitoring approach.

[1]  H. Müller,et al.  Kernel estimation of regression functions , 1979 .

[2]  T. McAvoy,et al.  Nonlinear principal component analysis—Based on principal curves and neural networks , 1996 .

[3]  F. E. Finch,et al.  Narrowing diagnostic focus using functional decomposition , 1988 .

[4]  W. Cleveland.  Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

[5]  Barry M. Wise,et al.  The process chemometrics approach to process monitoring and fault detection , 1995 .

[6]  J. Macgregor,et al.  Effect of impurities on emulsion polymerization: case I kinetics , 1988 .

[7]  Jie Zhang,et al.  Process fault diagnosis with diagnostic rules based on structural decomposition , 1991 .

[8]  John F. MacGregor,et al.  Process monitoring and diagnosis by multiblock PLS methods , 1994 .

[9]  Erkki Oja,et al.  Modified Hebbian learning for curve and surface fitting , 1992, Neural Networks.

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

[11]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems: theory and application , 1989 .

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

[13]  Daniel R. Lewin,et al.  Predictive maintenance using PCA , 1994 .

[14]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[15]  Jie Zhang,et al.  On-line process fault diagnosis using neural network techniques , 1992 .

[16]  M. Kramer.  Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[17]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[18]  Venkat Venkatasubramanian,et al.  A neural network methodology for process fault diagnosis , 1989 .

[19]  A. J. Morris,et al.  Confidence Bounds for Multivariate Process Performance Monitoring Charts , 1995 .

[20]  Jie Chen,et al.  Model-based methods for fault diagnosis: some guide-lines , 1995 .

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

[22]  K. A. Kosanovich,et al.  Applications of multivariate statistical methods to process monitoring and controller design , 1994 .

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

[24]  Jie Zhang,et al.  A self-learning fault-diagnosis system , 1991 .

[25]  Michael J. Piovoso,et al.  Process data chemometrics , 1991 .

[26]  Jie Zhang,et al.  Process performance monitoring using multivariate statistical process control , 1996 .

[27]  Jie Zhang,et al.  On-line process fault diagnosis using fuzzy neural networks , 1994 .

[28]  Masahiro Abe,et al.  Incipient fault diagnosis of chemical processes via artificial neural networks , 1989 .

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