Design of an induction motor fault diagnosis system based on BP neural networks

Induction motors are widely used in various fields of daily life and industrial production due to their simple power supply method, reliable operation, easy maintenance, and low cost, effectively freeing people from heavy labor. This paper focuses on induction motors as the research object. By reviewing a large amount of literature related to neural networks and motor fault diagnosis, we gained an understanding of the structure and working principles of induction motors. We summarized the common types of faults in induction motors and analyzed their mechanisms of occurrence. For the main faults such as stator winding inter-turn short circuit, broken rotor bars, and rotor eccentricity, we built an experimental platform to collect time-domain signals of induction motor faults and processed these signals. We designed an improved BP network and utilized fault samples to train the BP network and perform simulations on the MATLAB platform. Finally, we successfully completed the design of the induction motor fault diagnosis system.