Data description and noise filtering based detection with its application and performance comparison

Abnormalities should be detected reliably so that corrective actions can be taken to maintain a high quality level of products. Fault or outlier detection provides early warning for a fault and identification of its assignable cause. The availability of large real-time datasets has motivated the study of data-driven approaches to fault detection. Recently, many powerful kernel-based nonlinear learning techniques have been developed and shown to be very effective tools. As one of one-classification methods, SVDD is able to define a boundary around samples with a volume as small as possible. This paper proposes a data description-based detection method combined with orthogonal filtering for enhanced detection abilities. The orthogonal filtering as a preprocessing step is executed before SVDD modeling to remove unwanted variation of data. The performance of the proposed method was demonstrated using data of two test processes. The case study has shown that the proposed method produces reliable detection results. In addition, the use of SVDD combined with orthogonal filtering step outperformed other one-class classification-based detection methods.

[1]  S. Joe Qin,et al.  Multivariate process monitoring and fault diagnosis by multi-scale PCA , 2002 .

[2]  S. Wold,et al.  Orthogonal signal correction of near-infrared spectra , 1998 .

[3]  G. Baudat,et al.  Generalized Discriminant Analysis Using a Kernel Approach , 2000, Neural Computation.

[4]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[5]  Kwang-Jae Kim,et al.  Online monitoring and diagnosis of batch processes: empirical model-based framework and a case study , 2006 .

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

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

[8]  Zehang Sun,et al.  Object detection using feature subset selection , 2004, Pattern Recognit..

[9]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[10]  Roman Rosipal,et al.  Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space , 2002, J. Mach. Learn. Res..

[11]  Michael I. Jordan,et al.  Kernel independent component analysis , 2003 .

[12]  Rasmus Bro,et al.  Orthogonal signal correction, wavelet analysis, and multivariate calibration of complicated process fluorescence data , 2000 .

[13]  Theodora Kourti,et al.  Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS , 1995 .

[14]  Manabu Kano,et al.  Monitoring independent components for fault detection , 2003 .

[15]  P. Bishnoi,et al.  Fault diagnosis of multivariate systems using pattern recognition and multisensor data analysis technique , 2001 .

[16]  Age K. Smilde,et al.  Direct orthogonal signal correction , 2001 .

[17]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[18]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

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