TY  - JOUR
T1  - Classification Model using Neural Network for Centrifugal Pump Fault Detection
AU - Sayed, Eslam AU - A. Abdelsamee, Ahmed AU - M. Ghazaly, Nouby 
JO  - International Journal of System Signal Control and Engineering Application
VL  - 13
IS  - 4
SP  - 120
EP  - 126
PY  - 2020
DA  - 2001/08/19
SN  - 1997-5422
DO  - ijssceapp.2020.120.126
UR  - https://makhillpublications.co/view-article.php?doi=ijssceapp.2020.120.126
KW  - Centrifugal pump
KW  -fault detection
KW  -vibration fault classification
KW  -neural network and multi-layer perceptron
AB  - The different utilities of centrifugal pumps
made the potential for fault occurrence inevitable thus
early fault diagnosis is essential for such machines to
prevent further losses in different demands. In this study,
a vibration-based condition monitoring with the
development of the Artificial Neural Network (ANN)
model for fault classification and detection. The
Multilayer perceptron network with the back-propagation
algorithm model is that the most ordinarily used network
nowadays. The neural network ability to internally learn
from examples makes them more engaging and exciting
in the data mining scientific field, rather than following a
collection of rules such that by human consultants. This
paper deals with the evaluation and development of the
ANN model for fault recognition in a centrifugal pumping
system with two faults simulated which were seal and
particle impurities hitting the impeller. Data required to
feed the network extracted from the time-domain
vibration raw signal. Results showed great potential for
using ANN as a fault diagnosis; the recognition rate of the
network was 0.958.
ER  -