With the rapid development of robotics technology

machine learning has been applied to an increasing number of fields. Aiming at the importance of motors in industrial production and the energy loss and economic losses caused by faults

this paper proposes a study on a motor fault prediction model based on machine learning and energy storage optimization. By summarizing the application of machine learning algorithms in fault prediction and the impact of energy storage optimization strategies on energy utilization efficiency and model performance

a prediction model integrating multiple features is theoretically constructed

and its advantages are analyzed. The construction of this model helps improve the energy utilization efficiency and operational stability of motors in industrial production

thereby reducing the occurrence of faults.