PDF(Pubmed)
Abstract：
The purpose of this study is to put forward a feature extraction and pattern recognition method for the flow noise signal of natural gas pipelines in view of the complex situation brought by the rapid development and expansion of urban natural gas infrastructure in China, especially in the case that there are active and abandoned pipelines, metal and nonmetal pipelines, and natural gas, water and power pipelines coexist in the underground of the city. Because the underground situation is unknown, gas leakage incidents caused by natural gas pipeline rupture occur from time to time, posing a threat to personal safety. Therefore, the motivation of this study is to provide a feasible method to accelerate the aging, renewal and transformation of urban natural gas pipelines to ensure the safe operation of urban natural gas pipeline network and promote the high-quality development of urban economy. Through the combination of experimental test and numerical simulation, this study establishes a database of urban natural gas pipeline flow noise signals, and uses principal component analysis (PCA) to extract the characteristics of flow noise signals, and develops a mathematical model for feature extraction. Then, a classification and recognition model based on backpropagation neural network (BPNN) is constructed, which realizes the detection and recognition of convective noise signals. The research results show that the theoretical method based on acoustic feature analysis provides guidance for the orderly and safe construction of urban natural gas pipeline network and ensures its safe operation. The research conclusion shows that through the simulation analysis of 75 groups of gas pipeline flow noise under different working conditions. Combined with the experimental verification of ground flow noise signals, the feature extraction and pattern recognition method proposed in this study has a recognition accuracy of up to 97% under strong noise background, which confirms the accuracy of numerical simulation and provides theoretical basis and technical support for the detection and recognition of urban gas pipeline flow noise.
摘要：
本研究的目的是针对我国城市天然气基础设施快速发展和扩张带来的复杂情况,提出一种天然气管道流量噪声信号的特征提取和模式识别方法,特别是在有活跃和废弃管道的情况下，金属和非金属管道，天然气,水和电力管道共存于城市的地下。因为地下情况未知，天然气管道破裂引起的气体泄漏事故时有发生,对人身安全构成威胁。因此,这项研究的动机是提供一种可行的方法来加速衰老，对城市天然气管道进行更新改造，保障城市天然气管网安全运行，促进城市经济高质量发展。通过实验测试和数值模拟相结合,本研究建立了城市天然气管道流量噪声信号数据库，并利用主成分分析(PCA)提取流量噪声信号的特征,并建立了特征提取的数学模型。然后，构建了基于反向传播神经网络(BPNN)的分类识别模型,从而实现对对流噪声信号的检测与识别。研究结果表明,基于声学特征分析的理论方法为城市天然气管网的有序安全建设提供了指导,保证了其安全运行。研究结论表明,通过对不同工况下75组燃气管道流动噪声的仿真分析。结合地面流量噪声信号的实验验证,本研究提出的特征提取和模式识别方法在强噪声背景下的识别准确率高达97%,验证了数值模拟的准确性,为城市燃气管道流动噪声的检测和识别提供了理论依据和技术支持。
