Research Progress on Big Data Feature Mining of Electromagnetic Induction Effects in Electric Motors

Abstract

With the rapid development of Industry 4.0 and intelligent manufacturing, electromagnetic induction effects in motors, such as eddy current effects, harmonic distortion, and hysteresis losses, have become key factors affecting the energy efficiency and lifespan of motors. However, traditional analytical methods are highly dependent on limited working condition simulations and experimental data. This paper systematically reviews the application progress of big data technology in the study of electromagnetic induction effects in motors, with a focus on analyzing the advantages and disadvantages of traditional simulation, machine learning, and deep learning methods in feature extraction, anomaly detection, and optimization design. Research shows that data-driven technologies have significantly enhanced the accuracy and efficiency of energy efficiency optimization, fault diagnosis, and intelligent control. Future research needs to further integrate multi-source data with physical models to provide theoretical support and practical guidance for intelligent design, predictive maintenance, and energy efficiency optimization.