Diagnosing Faults in Electrical Machines Using VMD: The Effect of the Sampling Frequancy

Fault prediction in electrical machinery has become crucial due to their frequent use in harsh conditions, making them prone to various malfunctions that could lead to material and human losses. However, predicting faults requires high skills, especially when extracting faults from electrical signals, necessitating a robust analysis method. In this research, we relied on experimenting with the Variational Mode Decomposition (VMD) method. We applied it to an electrical signal of asynchronous current to diagnose rotor bar breakage faults, then calculated the uniform energy to determine the energetic changes occurring in the distorted signal frequencies and used them as features for classification. Due to the importance of time in prediction, we wanted to understand the extent to which reducing the length of the data affects the performance of VMD by changing the sampling frequency.