Virtual Prototyping Modeling and Fault Diagnosis Technology for Mechanical and Electrical Equipment

In order to study common faults in motors and motor transmission systems, this article uses a 5kW motor system as an experimental platform to establish a virtual prototype model. The prototype model includes the following five parts: motor unit, 6-degree of freedom loading mechanism, transmission gearbox, loading spindle, and AC excitation converter. Then, the BP neural network is used to identify typical faults in the virtual prototype. The final recognition time for vibration changes, temperature changes, and current disturbances does not exceed 45 seconds, with an average accuracy rate of over 99%. Overall, the algorithm can accurately diagnose typical faults in a relatively short time.