DICA enhanced SVM classification approach to fault diagnosis for
chemical processes
I. MONROYa, R. BENITEZb, G. ESCUDEROc, M. GRAELLSa.
a
Chemical Engineering Department, bAutomatic Control Department, cSoftware
Department.
EUETIB, Universitat Politècnica de Catalunya (UPC), Barcelona, SPAIN.
Presentation Type Preference: Oral Presentation.
Enter Theme/Sub Theme: On-Line Systems - Fault Diagnosis and Supervision
Keywords: Dynamic independent component analysis (DICA), SVM, Fault diagnosis
Type of contribution: Academy.
Presenter professional category: PhD Student.
As a result of the inherent dynamic and nonlinear characteristics of chemical process and their increasing
complexity, on-line monitoring and fault diagnosis are gaining importance for plant safety, process
economy, reliable maintenance and product quality.
Standard multivariate statistical monitoring methods, such as principal component analysis (PCA) and
partial least squares (PLS) do not explicitly take into account possible time correlations in the
observations or deviations from Gaussianity of the latent variables[1]. However, in most cases state
variables are driven by stochastic processes in the form of uncontrollable disturbances and random noise
which may present both auto and cross-correlation. In order to address this problem, dynamic principal
component analysis (DPCA) was proposed[2]. This approach constitutes a process monitoring method
that uses an augmenting matrix with time-lagged variables and has been shown to be valid in different
practical applications[3,4].
Recently, Independent Component Analysis (ICA) has been developed as a statistical technique that
extracts statistically independent components from multivariate observed data. By using higher order
statistical properties of the data, ICA provides a better identification of the underlying factors in the data
than standard PCA techniques[5,6,7].
The extension of DPCA to ICA led to a new method called dynamic independent component analysis
(DICA) which applies ICA to the augmenting matrix with time-lagged variables[3,8]. This method is able
to extract the major dynamic features or source signals from the process and to find statistically
independent components from auto- and cross-correlated inputs.
This paper addresses fault diagnosis by combining DICA and Support Vector Machines (SVM). DICA is
used for process monitoring and feature extraction in order to provide an enhanced representation of
process information. On a second stage, SVM is used for fault detection and diagnosis as a classification
algorithm. This hybrid DICA-SVM technique has been computationally implemented in MATLAB by
using the free packages FastICA[9] and light-SVM[10]. The resulting fault diagnosis system (FDS) is
then applied to the Tennessee Eastman[11] process as case study. Results are assessed by different
performance indexes widely accepted in the machine-learning and process control literature. Preliminary
results show that the proposed feature extension improves SVM classification performance, thus
indicating that the presented hybrid approach constitutes a very promising alternative for fault diagnosis
in chemical processes.
References:
1. P Nomikos and J.F Macgregor. AIChE Journal, 40, 8 (1994).
2. W-F Ku, R-H Storer and C Georgakis. Chemometrics and intelligent laboratory systems. Vol 30(1995).
3. J Lee, C Yoo and I Lee. Chemical Engineering Science 59 (2004).
4. J Chen and K Liu. Chemical Engineering Science 57 (2002).
5. R.F Li and X.Z Wang. Computers and Chemical Engineering 26 (2002).
6. L Jiang and S Wang. Proceedings of the third Conference on Machine learning and cybernetics,
Shanghai 26-29 (2004).
7. J Lee, S.J Qin and I Lee. AIChE Journal, 52, 10 (2006).
8. A Chen, Z Song and P Li. Lectures notes in computer science. Vol 3644 (2005).
9. Laboratory of Computer and Information Science. Helsinki University of Technology.
10.Thorsten Joachims. Department of Computer Science. Cornell University.
11.J.J Downs and E.F Vogel. Computers and Chemical Engineering, 17, 3 (1993).
En este trabajo se presenta una nuevo planteamiento de monitorización estadística de procesos basado en
la aplicación de Análisis de componentes independientes (ICA) y Análisis dinámico de componentes
independientes (DICA) con las Máquinas de Vectores de Soporte (SVM) para monitorizar procesos con
variables autocorrelacionadas y correlacionadas y mejorar el rendimiento de diagnosis de fallos.
El objetivo de ICA es encontrar una representación lineal de datos no gaussianos con componentes
estadísticamente independientes, mientras que DICA aplica ICA a una matriz aumentada con variables
retrasadas en tiempo, lo cual podría extraer la mayor dinámica del proceso. Las matrices de componentes
independientes son usadas como el conjunto de datos de entrada de las SVM para la diagnosis de fallos.
La metodología es aplicada a diagnosticar los fallos del caso de estudio TE y el mejor rendimiento de
diagnosis es obtenido usando sólo ICA+SVM con las condiciones que se usan en este artículo (ventana de
tiempo de 2 muestras para DICA).
Este trabajo será presentado en junio del presente año 2009.