Safety Early Warning Method for Pipes in Oil and Gas Field Stations Based on BP Neu-ral Network
The pipes in oil and gas field stations undertake the tasks of receiving,pressurizing,dis-tributing,pigging,metering,etc.,which are highly dangerous.Once abnormal conditions occur in the pipe,it may easily to cause major combustion and explosion accidents.Therefore,it is very impor-tant to monitor the status of the pipes in oil and gas field stations and give safety warnings.In this paper,based on BP neural network status monitoring data processing method,the safety early warning of pipe in oil and gas field stations is achieved according to the control chart theory.First of all,the strain data of pipes in oil and gas field stations are determined as the status monitoring indicator,and the strain monitoring data of pipes in oil and gas field stations are collected.Then,the BP neural network is used for simulation experiments to obtain and verify the pipe strain prediction model.Finally,the prediction data is screened by using the control chart,and the abnormal data is classified and graded.The safety early warning is carried out according to the hazard level category,and the safety plan is formulated to prevent the occurrence of dangerous accidents.The safety early warning method based on the BP neural network proposed in this paper uses status monitoring data for early warning analysis,and abnormal state data can be found at the early stage of risk occurrence,so as to take corresponding safety counter-measures.
pipe in oil and gas field stationsafety early warningstatus monitoringBP neural net-workcontrol chart theory
NETL
NSTL
万方数据
维普
