Research On Leakage Fault Diagnosis Method Of Distributed Pipeline Network Based On Multi-source Data

With the rapid development of China's industrial modernization,the consumption of petroleum and other fossil energy is increasing day by day.In order to carry out oil transportation safely and efficiently,pipeline transportation has become the best choice at present.Because of the special industrial attributes of petroleum,the safety and stability of its transportation equipment and system are very strict.In the process of oil transportation,if the pipeline breaks down and causes leakage failure,it will lead to a large number of casualties and economic losses.From the above analysis,it can be seen that it is of great significance to implement the necessary leakage fault diagnosis for oil pipelines.Therefore,according to the current situation and characteristics of oil pipeline and pipeline network,this thesis carries out further research on pipeline leakage fault diagnosis.The main innovations are as follows:Firstly,aiming at the characteristics of complex structure,large quantity of equipment and data of oil pipeline system,the data mapping relationship between pipeline equipment is analyzed.The coupling degree between pipeline status data and equipment information data in oil pipeline system is emphatically considered,and the operation model of oil pipeline system is designed.Moreover,the operation status of pipeline system is analyzed comprehensively,and the main equipment data in pipeline system are extracted.A distributed pipeline network leakage fault diagnosis system is designed in view of the large computation and data transmission load of the current pipeline leakage fault diagnosis system.The system realizes the real-time leakage monitoring of single pipeline in distributed station,and the overall fault diagnosis of the whole pipeline network is carried out by the system control center.Secondly,in view of the fact that the working conditions of oil pipelines are becoming more and more diverse,this thesis proposes a fault diagnosis method for pipeline leakage under complex working conditions based on Stacked AutoEncoder Network from the perspective of making full use of multi-source data of pipelines.First,a fault diagnosis network model of pipeline leakage based on Stacked AutoEncoder Network is established,including pipeline leakage detection model and pipeline leakage location model.Using actual pipeline data,the above fault diagnosis network model is trained to determine the parameters of the designed pipeline leakage detection model and pipeline leakage location model.Based on the real test pipeline data,the experimental simulation and analysis are carried out.The advantages of pipeline leakage fault diagnosis under complex working conditions are verified.Thirdly,on the basis of fault diagnosis of single pipeline leakage,considering the huge topology structure of oil pipeline network and the characteristics of interconnection between pipelines,this thesis proposes a fault diagnosis method of pipeline network based on Convolution Neural Network from the perspective of mining more effective features and reflecting local correlation of data.The network model of pipeline network fault diagnosis based on Convolution Neural Network is designed,and the forward and backward propagation of pipeline network data samples in Convolution Neural Network is analyzed.On this basis,the real data of pipeline network are analyzed and organized,and the fault diagnosis network model of pipeline network is trained.Then,the effectiveness of the fault diagnosis method based on Convolution Neural Network is verified by analyzing its fault diagnosis effect through experiments.