间歇性输送管道泄漏诊断方法研究
发布时间：2018-11-21 07:19
【摘要】：管道运输业的发展极大促进了化工工业和油气资源行业的发展,管道运输正逐渐成为油气资源运输的首选方式。与此同时,油气输送管线铺设逐年增加,服役期也在不断增长,由于施工缺陷、冲刷腐蚀、人为破坏、设备老化以及地理和气候环境变化等原因,会频繁导致油气运输管道介质泄漏事故的发生,从而造成了大量的资源浪费和财物损失。管道泄漏诊断技术是一项综合了多领域多学科知识的复杂性技术,特别是间歇性输送管道,管道工况极其复杂,并且受多种因素制约,并且到目前为止,还没有一种管道泄漏诊断方法是通用的,每一种方法都有它自身的适应性。因此,实现间歇性输送管道泄漏的可靠诊断具有重要的理论和实际应用价值。本文在时域统计特征分析和信息熵理论研究的基础上,提出了一种声波信号的特征提取方法。通过对声波信号统计特性的研究,提取了声波信号时域统计特征,描述了管道发生泄漏时,在频率分布直方图中表现出的“长拖尾”现象；对于实际运行管道频繁受到的输送工艺突发和持续的干扰,本文深入探究了信息熵理论在间歇性输送管道泄漏诊断中的应用,提出了声波信号频域功率谱熵和联合时-频域小波包空间特征谱熵的特征提取方法,完善了声波信号的特征描述；由于实际运行管道很难获取大量泄漏样本,本文管道泄漏诊断模型采用了具有单值分类特性的支持向量数据描述模型,通过对正常样本的训练,避免了泄漏形式多样化对模型泛化能力的影响。现场测试表明,本文所提出的间歇性管道泄漏诊断方法能够有效实现间歇性输送管道泄漏的快速、准确诊断,并且具有较高的可靠性、稳定性和工况适应性,为解决间歇性输送管道的泄漏诊断提供了一种有效、可靠的方法。
[Abstract]:The development of pipeline transportation industry has greatly promoted the development of chemical industry and oil and gas resources industry. Pipeline transportation is gradually becoming the preferred mode of oil and gas resources transportation. At the same time, the number of oil and gas pipelines is increasing year by year, and the duration of service is also increasing. Due to construction defects, erosion, artificial destruction, aging of equipment and changes in geographical and climatic environment, Oil and gas transportation pipeline medium leakage accidents occur frequently, resulting in a large number of waste of resources and property losses. Pipeline leak diagnosis technology is a complex technology which integrates multi-domain and multi-disciplinary knowledge, especially intermittent transportation pipeline. The pipeline working condition is extremely complex, and is restricted by many factors, and up to now, No pipeline leak diagnosis method is universal, and each method has its own adaptability. Therefore, the reliable diagnosis of intermittent pipeline leakage has important theoretical and practical application value. On the basis of time domain statistical feature analysis and information entropy theory, a method for feature extraction of acoustic signals is proposed in this paper. By studying the statistical characteristics of acoustic signals, the time-domain statistical characteristics of acoustic signals are extracted, and the phenomenon of "long trailing" in the frequency distribution histogram of pipeline leakage is described. For the frequent sudden and continuous interference of transportation technology in actual running pipeline, this paper probes into the application of information entropy theory in the diagnosis of intermittent pipeline leakage. The feature extraction method of frequency domain power spectrum entropy and combined time-frequency wavelet packet spatial characteristic spectrum entropy of acoustic signal is proposed to improve the feature description of acoustic signal. Because it is very difficult to obtain a large number of leakage samples in actual pipeline operation, the pipeline leak diagnosis model in this paper adopts the support vector data description model with the characteristics of single value classification, and through the training of normal samples, The influence of leakage form diversification on the generalization ability of the model is avoided. Field tests show that the method proposed in this paper can effectively diagnose intermittent pipeline leakage quickly and accurately, and has high reliability, stability and adaptability. It provides an effective and reliable method to solve the leakage diagnosis of intermittent pipeline.
【学位授予单位】：北京化工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TE973.6
本文编号：2346295
[Abstract]:The development of pipeline transportation industry has greatly promoted the development of chemical industry and oil and gas resources industry. Pipeline transportation is gradually becoming the preferred mode of oil and gas resources transportation. At the same time, the number of oil and gas pipelines is increasing year by year, and the duration of service is also increasing. Due to construction defects, erosion, artificial destruction, aging of equipment and changes in geographical and climatic environment, Oil and gas transportation pipeline medium leakage accidents occur frequently, resulting in a large number of waste of resources and property losses. Pipeline leak diagnosis technology is a complex technology which integrates multi-domain and multi-disciplinary knowledge, especially intermittent transportation pipeline. The pipeline working condition is extremely complex, and is restricted by many factors, and up to now, No pipeline leak diagnosis method is universal, and each method has its own adaptability. Therefore, the reliable diagnosis of intermittent pipeline leakage has important theoretical and practical application value. On the basis of time domain statistical feature analysis and information entropy theory, a method for feature extraction of acoustic signals is proposed in this paper. By studying the statistical characteristics of acoustic signals, the time-domain statistical characteristics of acoustic signals are extracted, and the phenomenon of "long trailing" in the frequency distribution histogram of pipeline leakage is described. For the frequent sudden and continuous interference of transportation technology in actual running pipeline, this paper probes into the application of information entropy theory in the diagnosis of intermittent pipeline leakage. The feature extraction method of frequency domain power spectrum entropy and combined time-frequency wavelet packet spatial characteristic spectrum entropy of acoustic signal is proposed to improve the feature description of acoustic signal. Because it is very difficult to obtain a large number of leakage samples in actual pipeline operation, the pipeline leak diagnosis model in this paper adopts the support vector data description model with the characteristics of single value classification, and through the training of normal samples, The influence of leakage form diversification on the generalization ability of the model is avoided. Field tests show that the method proposed in this paper can effectively diagnose intermittent pipeline leakage quickly and accurately, and has high reliability, stability and adaptability. It provides an effective and reliable method to solve the leakage diagnosis of intermittent pipeline.
【学位授予单位】：北京化工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TE973.6
【参考文献】
相关期刊论文 前2条
1 唐秀家,颜大椿;基于神经网络的管道泄漏检测方法及仪器[J];北京大学学报(自然科学版);1997年03期
2 陶洛文,方崇智,肖德云;以辨识为基础的长输管线故障定位[J];清华大学学报(自然科学版);1986年02期
相关硕士学位论文 前1条
1 朱爱华;卡尔曼滤波和序贯概率比检验在管道泄漏监测中的应用[D];天津大学;2006年
，本文编号：2346295
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