DICA enhanced SVM classification approach to fault diagnosis for chemical processes

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15 Οκτ 2013 (πριν από 3 χρόνια και 7 μήνες)

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DICA enhanced SVM classification approach to fault diagnosis for
chemical processes

I. MONROY
a
,
R. BENITEZ
b
,
G. ESCUDERO
c
, M. GRAELLS
a
.

a
Chemical Engineering Department,
b
Automatic Control Department,
c
Software
Department.

EUETIB, U
niversitat Politècnica d
e Catalunya

(UPC)
, Barcelona, SPAIN.

Presentation Type Preference: Oral Presentation
.

Enter Theme/Sub Theme: On
-
Line Systems
-

Fault Diagnosis a
nd Supervision

Keywords: Dynamic i
n
dependent component analysis (DICA
), SVM, Fa
ult diagnosis

Type of contributio
n: 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
-
correla
tion
. In order to address this problem,

dynamic principal
component analysis (DPCA
) was proposed[2]. This approach

constitutes

a process monitoring method

that

uses an augment
ing matrix

with time
-
lagged variables

and has been shown to be valid in different

practical applications[3,4]
.


Recently, I
ndependent
C
omponent
A
nalysis (ICA)
has been developed as

a

statistical

technique
that

extract
s

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
appl
ies

ICA to the augme
nting matrix with time
-
la
gged 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
V
ector
M
achines (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 diagnos
is as
a
classification
algorithm.

This hybrid
DICA
-
SVM technique
has been computationally
implemented
in MATLAB
by
using the free packages Fast
ICA
[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 Ji
ang 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. L
aboratory 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 pr
esenta 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 mon
itorizar 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, mientr
as 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.