Abstract:
With rapidly growing customer expectations, satellite antenna has become an integral
part of day-to-day life nowadays. Due to the growing demand, the number of installations
increase rapidly. When the installation quantity increases, the installation quality decreases
gradually. This leads to more customer complaints. Currently, a verification team does a
second-round check on the installation and the signal level. Even though this help in
identifying installation issues before customer complaints, this has become a failure due to
operational cost with the growing number of installations. The proposed method is based on
an image classification technique using Convolutional Neural Networks (CNN) along with a
transfer learning technique using the well-known model, VGG16. The experimental data set
was created by capturing images of correct and incorrect satellite installations. The correct
installation positions are covered with a set of predefined image templates (3 test scenarios).
Images of the satellite installations were captured according to the templates and they were
tagged as correct or incorrect by eye-bowling the installation. A basic CNN model, a VGG16CNN
model,
and
a
random
forest
classifier
were
compared
by
evaluating
the
model
accuracy
and
the
balanced
accuracy.
The
VGG16-CNN
model
was
selected
with
the
best
performance.
The
average
accuracy
and the average balanced accuracy of the final model are obtained as
85.8% and 86.1% respectively. This study was performed on a Windows 10 Pro 64-bit
machine with an Intel i5-7200U CPU operating at 2.50GHz and 16 GB RAM. The
prediction time was fast with a mean time of 0.5 seconds per image. Experimental verification
was done in the field and an average accuracy of 90.56% was obtained. With these prediction
models integrated, the operational cost can be reduced and the coverage can be increased
significantly.
Citation:
Perera, G.Y.V. (2022). Fault detection of satellite antenna installation using machine learning [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/22391