Fault diagnosis based on multivariate statistical techniques

In this paper, multivariate statistical techniques such as Fisher Discriminant Analysis and Generalized Discriminant Analysis are utilized for fault diagnosis in an industrial process. The pair-wise FDA analysis is used to identify the fault, which determines the most related variable with the present fault. Therefore, the FDA is proposed to classify linearly separable faults and the GDA to classify faults where a nonlinear classifier is needed. A new procedure to study faults is proposed which include wavelet analysis in the extraction phase, to reduce and decorrelate the data. A continuous stirred tank reactor was simulated in presence of typical faults in order to study the proposed method.

[1]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[2]  László Tóth,et al.  Kernel-based feature extraction with a speech technology application , 2004, IEEE Transactions on Signal Processing.

[3]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[4]  Manabu Kano,et al.  A new multivariate statistical process monitoring method using principal component analysis , 2001 .

[5]  Ramaswamy Vaidyanathan,et al.  Process fault detection and diagnosis using neural networks , 1990 .

[6]  Bhavik R. Bakshi,et al.  Multiscale SPC using wavelets: Theoretical analysis and properties , 2003 .

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

[8]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

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

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

[11]  C. Yoo,et al.  Overall statistical monitoring of static and dynamic patterns , 2003 .

[12]  D. Seborg,et al.  Pattern Matching in Historical Data , 2002 .

[13]  Arthur K. Kordon,et al.  Fault diagnosis based on Fisher discriminant analysis and support vector machines , 2004, Comput. Chem. Eng..

[14]  Anastasios N. Venetsanopoulos,et al.  Kernel Discriminant Learning with Application to Face Recognition , 2005 .

[15]  Juergen Hahn,et al.  Fault detection and classification in chemical processes based on neural networks with feature extraction. , 2003, ISA transactions.

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

[17]  Q. Peter He,et al.  A New Fault Diagnosis Method Using Fault Directions in Fisher Discriminant Analysis , 2005 .