POSSIBILITY OF APPLICATION OF POSSIBILITY OF APPLICATION OF ARTIFICIAL NEURAL NETWORK ARTIFICIAL NEURAL NETWORK

FOR RECOGNIZING OF ACOUSTIC-FOR RECOGNIZING OF ACOUSTIC-EMISSION EVENTSEMISSION EVENTS

Pirumov А.1 , Chvertko Ye.1, Por G.2 , Dobjan T. 2

1 National Technical University of Ukraine "KPI", Kyiv, Ukraine2 College of Dunaújváros, Dunaújváros, Hungary

Advantages and features of AE Advantages and features of AE monitoringmonitoring

allows to detect and identify developing defects;

allows to control the entire object using a small number of stationary sensors on its surface

background noiseno database with the results of systematic

studiesthe need of operator

Typical information parameters Typical information parameters for the AE signals analysis for the AE signals analysis include:include:

EXPERIMENTSEXPERIMENTS

Two series of experiments:

Source of Hsu-Nielsen (graphite rod breaking)

Concentrated metal bullet impact

EXPERIMENTSEXPERIMENTS

NEURAL NETWORKNEURAL NETWORK

S1×1

a1

Input

II ndist II

IW1,1

LW2,1C

Competitive layer Linear layer

P

R×1

S1×R

S1×1

R S1

S2×1S2×S1

S2

n2

a2 = y

S2×1

ni1 = II iIW1,1 – P II

a1 = compet (n1)

ai2 = purelin (LW2,1 a

1)

n1

NEURAL NETWORKNEURAL NETWORK

Selection of network type and structure

Neural network training Neural network application

Data

Data processing

results

Adjusting of weight

coefficients

Data

Training method and parameters

Goals vector

NEURAL NETWORKNEURAL NETWORK

START

Initializing the neural network:net = newlvq (minmax (P), 12, [.625 .375], 0.1);

Generation of training vectors using the experimental data (vector P)

Generation of goals vector and transformation into a matrix:

Тс = [1 1 1 1 2 2 2 …]T=full(ind2vec(Tc))

Specifying parameters of training:net.trainParam.epochs=2000;

Neural network training:net = train (net, P, T)

END

RESULTS OF THE FIRST TESTRESULTS OF THE FIRST TEST

Results of identification of experimental data which weren’t used during network training

APPLICATION TO TENSILE TESTAPPLICATION TO TENSILE TEST

AE Sensors

РР

APPLICATION TO TENSILE TESTAPPLICATION TO TENSILE TEST

CONCLUSIONSCONCLUSIONS

Application of artificial neural networks makes it possible to identify AE events sources with an accuracy of at least 75 %, giving wide opportunities for automation of the AE control

The preliminary experiments have shown the possibility of determining the state of the material under mechanical loads by analyzing the AE signals using artificial neural networks