Research on Electrical Equipment Automatic Control System Supported by Machine Learning
This study constructs a machine learning-driven intelligent control framework for electrical equipment,utilizing data and algorithms to evaluate and predict the operating status of the equipment.The research validates the effectiveness of state monitoring and fault prediction models based on deep learning and random forests.The challenges for the next stage of work lie in promoting technological breakthroughs in few-shot learning for industrial scenarios and expanding the application of the framework to more complex environments.
machine learningelectrical equipmentautomatic control system
NETL
NSTL
万方数据
