| As a clean,efficient and convenient green energy,natural gas plays an important role in China’s energy structure adjustment strategy.The use of natural gas can reduce the emission of sulfur dioxide,nitrogen oxides and other pollutants,which effectively improves the ecological environment in China.With the increasing of China’s economic strength and national environmental awareness,natural gas as a high-quality energy,its proportion in energy consumption is getting higher and higher.Ensuring the safe transportation of natural gas is the basis of satisfying the reliable use of natural gas by residents.Pipeline transportation with economic,stable and efficient advantages,has become one of the most widely used natural gas transportation.However,due to the influence of weather and geographical location,when the external temperature is too low,hydrate blockage is easy to occur in the position with high transmission pressure of natural gas pipeline.In addition,due to pipeline corrosion and aging,excessive fluid scouring force,welding defects and construction damage,pipeline will appear the problem of natural gas leakage.Once pipeline leakage or hydrate blockage occur in the process of natural gas pipeline transportation,it will not only cause huge economic loss and resource waste,but also endanger personal safety.Therefore,it is of great economic value and social significance to carry out research on the safety monitoring of natural gas pipeline.In this paper,the acoustic excitation signal and classification algorithm of natural gas pipeline safety monitoring are studied,including the study of natural gas pipeline safety monitoring system,the acoustic excitation signal of natural gas pipeline safety monitoring and the classification model and algorithm of natural gas pipeline events.Firstly,the principle of natural gas pipeline safety monitoring is described,and the software and hardware platform of natural gas pipeline safety monitoring system is built based on the principle.Then,the acoustic excitation signal of natural gas pipeline monitoring is studied,and the signal-to-noise ratio of monitoring signal is effectively improved by using orthogonal complementary Golay coding excitation signal.It mainly includes orthogonal complementary Golay coding signal excitation principle,simulation experiment and result analysis,and gas pipeline abnormal event monitoring experiment.The experimental results show that the SNR of pipeline blockage signal is as high as 29.19 d B,and the SNR of pipeline leakage signal is as high as 28.79 d B.Finally,the neural network model is used for the classification and recognition of natural gas pipeline events,and the performance of the optimal network model is compared and analyzed with the evaluation index of the classification model.For the classification and recognition of non-aliasing pipeline events,both 1D-CNN and LSTM network model can reach 100% accuracy.The advantage of 1DCNN model lies in the running time.For the classification and recognition of aliasing pipeline events,the accuracy of 1D-CNN model can still reach 100%,while the LSTM network model does not achieve the same effect and the accuracy of LSTM network model is only 80.56%.Therefore,one-dimensional convolutional neural network has better classification effect and wider application prospect. |
