7 th INTERNATIONAL CONGRESS ON ENGINEERING AND TECHNOLOGY MANAGEMENT, İstanbul, Turkey, 16 - 18 April 2022, pp.1-8, (Full Text)
Rotary electric machines are widely used in industry, energy production,
renewable energy sources and factories, mainly alternating current (AC)
machines and direct current (DC) machines. Therefore, any scientific study that
can be done on rotating electrical machines will contribute to
industrialization. Although the rotating electric machines vary according to
the manufacturers, the ones with the same power are very similar to each other
in terms of external appearance. In systems that require remote control or
monitoring with a screen, it is sometimes necessary to determine the type of
rotating electrical machine immediately. Label values may not be read on the
screens or the label may remain outside the camera dial while it is in
operation. In this study, a deep learning (DL) method, a convolutional neural
network (CNN) model, was implemented in order to classify and detect rotating
electrical machines with only external appearance for such special cases. In
the test results after the training of the model, success was achieved with an
accuracy rate of 87.5%. It has been observed that the use of the proposed DL
model in the classification and species detection of rotating electrical
machines gives very successful results.