Leakage Aperture Identification Of Natural Gas Pipelines Based On Transfer Learning

Natural gas is a major energy source needed for national economic development and human lives,and natural gas pipelines are a key component in transporting fuel to factories,manufacturing facilities and residents.Leaks caused by aging and corrosion of pipeline materials have become an important factor affecting the normal operation of pipelines.Accurate identification of the leak aperture size will help the safety operation department to assess the leak hazard and develop repair measures to reduce the damage,so it is important to conduct research on the identification of leak apertures in natural gas pipelines.In this paper,deep convolutional neural network and transfer learning theory are introduced into pipeline tiny aperture recognition to provide new ideas for intelligent aperture recognition applications.The main research contents are as follows.First,the hazards of natural gas pipeline leakage are discussed;then the development and current situation of pipeline leakage detection is investigated in depth,and the application of transfer learning research is investigated;finally,the process of intelligent leakage aperture recognition,deep convolutional neural networks and the theoretical basis of transfer learning are described.Aiming at the problems of difficult training of deep neural networks and large differences between training data and actual collected data distribution,this paper introduces domain adaptation into leak aperture recognition,and studies a feature space domain adaptation leak aperture recognition method based on deep learning and transfer learning.To give full play to the feature extraction capability of convolutional networks,the method uses the continuous wavelet transform method to convert the vibration signal into a time-frequency map.The domain adaptation network constrains the network to learn domain invariant features by minimizing the differences in feature distribution and label distribution,and finally achieves the identification of pipe leakage apertures in complex environments.With the assistance of training data with labels,the deep residual network can effectively find the inherent laws of the actual sampled data without labels and automatically extract the detailed features of leaky apertures to improve the recognition accuracy of the network in practical applications.To solve the problems of insufficient amount of labeled data and low utilization of multiple data sets,this paper investigates a leaky aperture identification method based on multi-source domain transfer.The method is based on the extraction of features by residual convolutional networks and the integrated use of multiple source domain specific classification networks to extract features and diversify the expression of target domain features.The shallow domain invariant features of multiple source and target domains are first extracted and each source-specific classifier is constructed,and then the source domain classification results are weighted and merged by a multi-source domain coordinator to finally complete the leaky aperture identification of the target domain data.The research method proposed in this paper was validated using a laboratory platform dataset.In this paper,the main parameters of the proposed method and their effects are analyzed in more depth through several comparative experiments and compared with other methods,and the experimental results reflect the effectiveness of the research method in this paper.