@article{MAKHILLIJSSCEA202013428800,
    title = {Classification Model using Neural Network for Centrifugal Pump Fault Detection},
    journal = {International Journal of System Signal Control and Engineering Application},
    volume = {13},
    number = {4},
    pages = {120-126},
    year = {2020},
    issn = {1997-5422},
    doi = {ijssceapp.2020.120.126},
    url = {https://makhillpublications.co/view-article.php?issn=1997-5422&doi=ijssceapp.2020.120.126},
    author = {Eslam,Ahmed and},
    keywords = {Centrifugal pump,fault detection,vibration fault classification,neural network and multi-layer perceptron},
    abstract = {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.}
    }