LAUSR

Abstract With the scale expansion and structural upgrading of pipeline network, the detection methods based on both ends of the pipeline pressure have appeared the limitations of judging pipeline status in the multi-mode and complex network system. To overcome the limitation of early methods, a pipeline network status detection method based on data transformation convolutional neural network (DT-CNN) is proposed in this paper. Firstly, the difference among the eigenvalue distribution of data covariance matrices is calculated to detect the pipeline status by Kullback-Leibler divergence (KLD). If the eigenvalue distribution deviates from the normal status, the KLD will exceed the given threshold. Furthermore, an improved CNN model is proposed to judge pipeline status by converting the largest eigenvectors of data covariance matrices to extract features. The effectiveness of the proposed detection method is demonstrated through the simulation results of a practical pipeline network.