Prediction of Power Operation Tool Wear State Based on BP Neural Network
With the increasing number of tasks in the distribution network,the wear rate of power operation tools continues to rise.Traditional state monitoring methods based on regular electrical testing and appearance inspec-tions before use are gradually unable to meet the actual demands of current tool wear,leading to certain safety hazards.Therefore,the prediction of tool wear states is of great significance for improving operation and mainte-nance efficiency and reducing operational risks.This study proposes a power operation tool wear state prediction model based on the BP neural network,which utilizes historical data for model training to achieve accurate predic-tions of tool wear states.This provides strong technical support for the operation and maintenance management of power operation tools.
distribution networkBP neural networkpower operation tools
NETL
NSTL
万方数据
