Analysis of methods for diagnosing asynchronous electric motors used in agriculture

Abstract

This study aimed to examine methods for diagnosing asynchronous electric motors in order to determine their effectiveness in detecting various types of faults. The research methodology involved an analysis of the effectiveness of diagnostic methods for electric motors, including vibration diagnostics, infrared thermography, noise diagnostics, and electrical diagnostics. This analysis was conducted based on a comparative assessment of their informativeness in detecting faults. The fundamental principles of the diagnostic methods and their physical bases, as well as their advantages and limitations in the context of detecting defects such as rotor imbalance, bearing damage, winding short circuits, component overheating, and other typical faults, were identified. In the analysis of electrical diagnostic methods, their versatility was noted, enabling the detection of a wide range of defects through the analysis of changes in electrical signals. It was observed that the Fast Fourier Transform method is optimal for static conditions, whereas the Short-Time Fourier Transform provides the capability to analyze dynamic changes, and the Gabor Transform allows for the detection of subtle signal changes. The results obtained provide a scientific foundation for optimizing the process of diagnosing asynchronous electric motors and ensuring their more effective use in industrial and agricultural settings.