A Comparative Analysis of Fault Diagnosis by vibration signals for Critical Gear Components in Electric Vehicle Motor Testing Machines Using Machine Learning Algorithms

Electric vehicles (EVs) represent the future of the automobile industry, with the drive motor being a crucial component in modern EVs. To ensure optimal performance, a motor testing machine is used to verify the essential parameters of these electrical motors. This machine regularly evaluates electric motor characteristic curves and their electromagnetic behavior under different conditions. However, it has been observed that flaws in the testing machine's helical gear arrangement frequently cause malfunctions and suboptimal performance, affecting the efficiency of the electric vehicle production line. This research project aims to improve the motor test bench apparatus by modifying critical design parameters using machine learning and vibration signal analysis methods. Vibration signals are recorded at various gear settings, and statistical features are extracted from these signals. These signals are then classified using classifiers such as Naive Bayes and Decision Trees. Machine learning algorithms are employed to categorize the collected signals as normal or faulty, both with and without a 0.25 KW load applied in each condition. The performance of several algorithms is compared and analyzed. The results show that the Naive Bayes technique outperforms the other algorithms, achieving an impressive accuracy of 95.2%. Therefore, the proposed approach offers an effective way to enhance the functionality of the motor test bench