With the improvement of industrial intelligence, electrical equipment, as a core infrastructure in industrial production and energy supply, directly affects production safety and economic benefits through its operational stability. However, under long-term high-load operation, electrical equipment is prone to faults such as insulation aging and component wear. Traditional diagnostic methods rely on human experience, resulting in problems such as slow response, low diagnostic accuracy, and difficulty in handling massive amounts of operational data, failing to meet the real-time early warning needs of modern equipment operation and maintenance. This paper aims to study big data diagnosis and early warning of electrical equipment faults. First, it constructs a big data collection and preprocessing system for electrical equipment operation to achieve standardized processing of multi-source heterogeneous data. Second, it proposes a hybrid machine learning diagnostic model that integrates random forest and long short-term memory network (LSTM) to improve fault identification accuracy by combining the temporal operation characteristics and static attribute characteristics of the equipment. Finally, it designs a hierarchical early warning mechanism based on the model output confidence and fault evolution law. Experimental results show that the improved random forest algorithm performs best in fault diagnosis of various types of electrical equipment, with an overall diagnostic accuracy of 98%, which is significantly improved compared to 92% for traditional random forest, 88% for support vector machine, and 85% for BP neural network, providing reliable decision support for electrical equipment operation and maintenance.
Zhengwei Li (Thu,) studied this question.