Fault detection scheme using neural networks with

FAULT DETECTION SCHEME USING NEURAL NETWORKS WITH ...

14th World Congress of IFAC

K-3e-14-6

Copyright 0 1999 IFAC 14th Triennial World Congress, Beijing, P.R. China

FAULT DETECTION SCHEME USING NEURAL NETWORKS WITH FUZZY PREPROCESSING

Francklin Rivas-Rcheverria* Mariela Cerrada-Lozada** Danny Aguilar-Morales

[iniversidad de Los Andes Facultad de Ingenieria

Escuela de Ing. de Sisremas Dpto. de Sistelnas de Control Merida - Venezuela 5JOl Phone:(58) 74 402847 Fax:(58) 74 402846 * e-mail: rivast{iUng.uta.ve * * e-mail: cerradam(ij)ing.ula.ve

Abstract: In this work it is presented a fault detection scheme usjng neural networks \vith a fuzzy preprocessing done to the input signals. The use of the fuzzy preprocessing enriches the input set given to the fault diagnosis neural network. We present an example of a three tanks system fault diagnosis using a conventional neural net\vorks schenle and the proposed one. Copyright CO 1999 IFAC Kcy'-vvords: Neural

Netvvorks~ Signal

Processing, Fuzzy

Logic~

Fault Detection.

]. INTRODl;CTION Neural net\,·orks have been widely used in many industrial applications such as: Contra1 systems (Narendra and Parthasarathy, 1991), Identification (Narendra and Parthasarathy,1990), Pattern recogn [tion (Co Iina, 1994) and fault detection and diagnosis (Hoskin et aI, 1991; Vergara et al~ 1997; Watanabe and Himmclblau,] 983). Some of the reasons for using neural networks are: Can '~Leam" from historical data, so they can be used as associative memories. They have great generalization capabilities, so they can give accurate outputs for input patterns different that the used in the training fase. They call be used with corrupt or inconlplete dat.a, because the "kno\v)cdge" is spread over the network s interconnection weights. Can give input/output maps from data \vithout apparent relation. They are easy for COll1puter implantation. There exist a great nUlnber of learning algorithnls that can be used for specific problems.

On the other qand, fuzzy Jogjc (Zadeh, ] 965) cnlu]ates the human classification capabilities using multivaluated criteria instead of the classical binary log1c used in computational environments. Fuzzy logjc creates some fuzzy sets whjch are described using linguistic labels and a membership Jevel \vith values between lO,] J according to the real partial belonging to each of the created fuzzy sets. In this work it is proposed a neural networks fault diagnosis scheme with signal fuzzy preprocessing. This is, the measured signals arc used in a fuzzification process for obtaining the membership level of each signal to the different fuzzy set. These ll1embership levels are used as inputs for the fault djagnosis neural networks. The fuzzjfication process used on the measured signals gives to the neural networks richer infonnation for detecting the operational condition. This ,",\lork is organized as follows: Section 2 presents in detail the proposed fault detection scbelne using

5543

Copyright 1999 IFAC

ISBN: 0 08 043248 4

FAULT DETECTION SCHEME USING NEURAL NETWORKS WITH ...

14th World Congress of IFAC

neural networks with fuzzy preprocessing. Section 3 contains an example considering some possible valves faults in a three interconnected tanks system. FinaJly, in section 4 we give some concluding remarks.

The dynamical equations that describes the normal operation of the system are:

2. FAULT DETECTION SCHEME USING NEURAL NETWORKS WITH FUZZY PREPROCESSING. Figure ] illustrates the proposed fault detection scheme. The process sampled signals obtained using a data acquisition system are incorporated in a fuzzification process where is obtained the signal membership levels to each fuzzy set created according to the variability of the process signals. The fuzzification process increases the number of the inputs that are going to be given to the neural network that is going to evaluate the process operation conditions (nonnal operation 0 any of the possible faults). With this increased Ilunlber of inputs to the neural network, it is enriched the training patterns used for Hteaching" the possible faults that can occur on the system. x1

The possible faults that we have consider are about the blocking or the valves gO, q 1, q2 or q3~ than can occur individually or simultaneously. In this example \ve just consider the fault associated \vith valves q2 and q3.

Normal OF ~tion

--------t------~

Measured Prcces;

where: hI, h2, h3 are the tanks levels. u(t) is the caudal received by the first tank. Ri and Ai are the restriction of each valve and the area of each tank. respectively.

N.N.

Fa.uLt 1 faulll

Fault Detection

Fawtc

When the valve q2 is blocked (Fault 2), The model of the system is:

·

(Ro+R1](IJ ] A-I hj(t)+ROA

hl(t)=-l-RoR~

Fuayfi~ation

Prates::

Slgna1S

h 2 (t)

Fig. ]. fault Detection Scheme using Neural Nenly'orks \vlth Fuzzy Preprocessing.

•

_

h ~ (l)

In order to prove the fault detection scheme \vith fuzzy preprocessing capabilities compared with a classicaJ neural scheme, we present an example about a Three IntercOImected Tanks System, as the one illustrated on Figure 2. Tank I receives a fluid ,"vith constant caudal and froTTI this tank the other t~'o are filled.

i :-A~l ~

1 - h? (t) R oA 2 -

1

= --- ----- --- h I (t) - -R oA

= --

2

1

-- -

R 1A

3

h I (t) +

1 R 2A

1_

3

h 2 (t) - -- --- --- h:1 (t) R 3A 3 (3.2)

3. A.PPLICATfON EXA.MPLE:

-u(l)

]

h 2 (t)+ AI u(t) j

And wben the broken valve is q3 (Fault 3) the model is:

l]

P"

RO

I

.

l'

~I /

~~t 1~7-