A Comparative Study on fault diagnosis of Electrical vehicle motor testing machine Gear Components Using machine Learning algorithms.

Electric vehicles (EVs) are the way of the future for the automobile industry. The drive motor is a vital part of modern EVs. A motor testing machine is used to verify the critical parameters of the electrical motor, which is seen to be of the utmost importance. Electric motor characteristic curve points and their electromagnetic behaviour under different conditions are regularly found using the motor testing machine. But it has been noted that a flaw in the testing machine's helical gear arrangement causes frequent malfunctions and less-than-ideal performance, which impacts the assembly line's effectiveness in producing electric cars. The research project is to enhance the motor test bench apparatus by modifying crucial design parameters using machine learning and vibration signal analysis methods. Vibration signals are recorded at various gear settings before statistical features are extracted. Following that, these signals are classified using classifiers such Quadratic SVM and Bagged Trees. The collected signals are classified as normal or faulty using machine learning algorithms, both with and without a 0.25 KW load added for each condition. Several algorithms' performances are compared and examined. With an astounding accuracy of 95.3%, the data show that the Bagged Trees approach works better than the other algorithms. As such, the suggested approach offers a clever way to increase the motor test bench's functionality.