Gas pipeline event classification based on one-dimensional convolutional neural network

Abstract

Pipeline block and pipeline leak may lead to serious accidents and cause huge economic losses, which have been urgent problems for gas transportation. In this work, active acoustic pulse-compression technology is first introduced to detect and locate these two anomalous events. The matched filtered signals are then normalized and input into one-dimensional convolutional neural network to achieve classification of not only pipeline block and pipeline leak but also normal event such as pipeline elbow which causes acoustic wave reflection as well. Neural network parameter optimization has also been carried out as well as the comparison with long- and short-term memory network. Experimental results demonstrate that compared with long- and short-term memory network, one-dimensional convolutional neural network has an improvement in efficiency due to the great reduction of running time. For non-aliasing pipeline events, both of the models can reach 100% classification accuracy. For aliasing pipeline events, despite the shorter time series and fewer features, the classification accuracy of one-dimensional convolutional neural network still reaches 100.00%, but that of long- and short-term memory network is only 93.89%. Furthermore, the smoothing and slight fluctuation of receiver operating characteristic curve and the high value of area under curve also verify the stability and good classification performance of the proposed trained model. Therefore, the one-dimensional convolutional neural network shows significant performance for pipeline events classification and has considerable potential and application prospect in gas pipeline safety monitoring.

Source location and anomaly detection for damage identification of buried pipelines using kurtosis-based transfer function

“…These studies involve autoregressive-moving-average-based vibration characteristic monitoring and analysis to diagnose the condition of subsea pipelines, 23 monitoring rapid water pressure fluctuations that cause damage to waterworks pipelines, 24 monitoring hydraulic pipe damage for aircraft engines using fiber Bragg grating sensors and Kalman filters, 25 and classifying convolutional neural network-based leakage signal for pipe damage monitoring. 26 As indicated in these examples, the primary goal of these studies was to improve the accuracy of leakage detection or prevent damage through damage source detection. However, although prompt response and prevention through constant monitoring are important to prevent the impact damage that causes sudden pipeline failure, there is a lack of relevant research on this topic.…”