EARLY DETECTİON OF DAMAGE İN ELECTRİC VEHİCLE MOTORS USİNG ARTİFİCİAL INTELLİGENCE METHODS

The rapid development of electric vehicles has increased the importance of reliable and efficient electric motor operation. Electric motors are among the main components of electric vehicles, and their failures can significantly affect vehicle performance, energy efficiency, driving safety, and maintenance costs. Therefore, early detection of motor damage is essential for preventing serious failures and reducing unexpected maintenance requirements. This study investigates the application of artificial intelligence methods for the preliminary detection of faults and damage in electric vehicle motors. The proposed approach considers the analysis of measurable motor parameters, including current, voltage, temperature, vibration, rotational speed, and torque. These parameters can be collected through sensors during different operating conditions and processed using machine learning and deep learning algorithms. Methods such as Artificial Neural Networks, Support Vector Machines, Random Forest, and Convolutional Neural Networks can be used to classify normal and faulty operating conditions.