Using Artificial Intelligence (AI) for Monitoring and Diagnosing Electric Motor Faults Based on Vibration Signals

Detecting bearing faults in electric motors is highly crucial for improving production efficiency and reducing accidents in complex mechanical systems, which poses significant challenges for current fault diagnosis technology. This paper investigates and applies Artificial Intelligence (AI) to enhance the monitoring and diagnosis process of electric motor faults based on vibration signals. The research aims to construct a model for collecting sample data from motors with three common types of bearing faults and utilizes the Resnet-50 network to assess the accuracy of monitoring and diagnosing faults. The study conducts a vibration signal analysis to identify potential indicators of faults in electric motors. The survey results presented in the paper demonstrate the accuracy of using the Resnet-50 network in monitoring and diagnosing electric motor faults. The paper also provides essential insights into the performance of AI networks and their practical applicability in the field of industrial equipment maintenance and management.