Rotary electric machines are one of the indispensable elements used in industry today. The advancement of technology and the subsequent emergence of more energy needs have led to the use of more rotary electric machines and especially generators in the industry. The increasing use of rotating electrical machines in the industry naturally increases the number of possible malfunctions. The most basic failure condition in rotating electrical machines is stator winding failures. Stator winding faults can show itself in the form of stator windings burning due to reasons such as excessive current or overload, or stator windings breaking due to other reasons or wear of insulations.
Today, developments in the fields of computer and artificial intelligence offer the opportunity to conduct research on the automatic detection and solution of every problem. In the light of the developments in artificial intelligence technology, in most of the scientific studies conducted by researchers in the scientific world, it is aimed to save time and solve the problem by applying artificial intelligence to every problem. Stator winding faults can normally be noticed at first glance by any winders or electrical machine researchers. However, some stator faults are not immediately noticeable and cause loss of time in the troubleshooting process. In order to solve this problem to some extent, an image-based artificial intelligence application was carried out in this study. A deep learning model has been applied that can detect stator winding faults with high accuracy based on the image. As a result of the study, it has been seen that image-based deep learning approaches can be used in automatic fault detection in rotary electric machines and stator winding failure can be distinguished with a high accuracy rate and this application can be developed by generalizing it. ORCID NO: 0000-0001-9105-508X
Anahtar Kelimeler: Electric Machines, Stator Winding, Deep Learning, CNN,