| Long-distance oil and gas pipelines are the main mode of transportation of oil and natural gas energy.This mode of transportation has high efficiency and low cost.However,due to corrosion,wear and damage from various external forces,pipeline leakage accidents occur frequently,causing economic losses to the country,bringing security risks to the people.Pipeline Magnetic Flux Leakage(MFL)is one of the main detection methods for long-distance oil and gas pipelines.The MFL in-pipe detector detects the MFL signal at the pipeline defect,so as to analyze and identify the damage degree of the pipeline defect.At present,there are many pipelines to be inspected,and the pipelines are long,resulting in a large number of pipeline defect MFL signals that need to be identified.At present,the identification of MFL signals mainly adopts manual interpretation,which is time-consuming and labor-intensive and easy to cause misdetection and missing detection.Obviously,manual interpretation can no longer meet the engineering needs,so it is urgent to develop an intelligent defect identification method to improve the efficiency of defect identification.Deep learning can learn the characteristics of input data,and it is widely used in classification,quantitative recognition and other fields.This paper takes the MFL signal data at pipeline defects as the research object,and uses the basic framework of deep learning to intelligently identify the MFL signals at pipeline defects and identify the size of the defect.Aiming at the problem of intelligently identifying the defect size of long-distance oil and gas pipelines and effectively evaluating the degree of damage to the pipeline,a deep learning-based intelligent identification and processing method of defect magnetic flux leakage data is proposed.This method uses a deep convolutional neural network,takes the structured data of the magnetic flux leakage detection signal at the pipeline defect as the input source for quantitative analysis of the model,and normalizes the defect sample data,which can effectively reduce the influence of detection interference.The convolutional neural network model mainly includes 4 convolution layers,4pooling layers,3 fully connected layers and 1 output layer.The convolution kernel is used to extract the magnetic flux leakage detection data features at the defects of the pipeline,and the output of the output layer is improved.In this way,the activation function of the output layer is different from the Softmax classifier used for classification and recognition,and the recognition results of the network model are linearly output to realize intelligent recognition of the size of pipeline defects.The experimental results show that the proposed method has a good ability to quantify the length and depth of pipeline defects,the quantification error of defect length is 2-7mm,and the quantization error of defect depth is 1-7mm,which can meet the needs of engineering quantification.The rapid batch identification of engineering data has a good application prospect in the field of pipeline magnetic flux leakage detection data processing. |
