基于EEMD和支持向量机的天然气管道泄漏诊断
发布时间：2019-04-18 12:17
【摘要】：随着天然气战略资源作用和地位提升,运输管道的运行安全越来越受到人们的重视。管道在运行维护时,需要进行实时监测,准确判断管道运行状况,及时诊断泄漏隐患,避免安全事故。虽然管道泄漏检测技术在不断改善,但是在管道泄漏检测中,仍然会出现错报、漏报、误报的情况。因此,本文针对这一情况,给出天然气管道泄漏诊断的设计方案,最终实现管道泄漏高准确率的智能诊断。本文利用经验模态分解EMD方法能够将原始信号依照不同的频率尺度下逐级分解,将这些不同尺度的波动或趋势提炼形成本征模态函数,再对能够体现原始信号特性的各个本征模态函数提取能量熵和近似熵特征特征。由于传统经验模态分解方法中存在模态混叠效应的弊端而进行深入分析,最终采用总体经验模态分解EEMD方法和近似熵、能量熵相结合方法进行特征提取。应用支持向量机对四种特征提取方法提取的特征向量组进行模式识别诊断分析,并进行识别效果对比,判断最佳的特征提取方式。支持向量机识别诊断方法的复杂度和泛化能力由惩罚因子C和核函数参数g决定的,为了提高识别诊断的准确率,需要一个精确、快速、稳定的方法来寻找最优参数。本论文应用Libsvm软件平台分别采用网格搜索参数寻优、粒子群算法、遗传算法以及粒子群与遗传结合算法对支持向量机管道泄漏类型分类的惩罚因子C和核函数参数g进行优化,并进行分类准确率效果对比,最终达到高准确率模式诊断的目的。
[Abstract]:Along with the natural gas strategic resource function and the status promotion, the transportation pipeline operation safety receives the people's attention more and more. When the pipeline is running and maintaining, it is necessary to carry on real-time monitoring, accurately judge the running condition of the pipeline, diagnose the hidden danger of leakage in time, and avoid the safety accident. Although pipeline leakage detection technology is constantly improving, but in pipeline leakage detection, there will still be misreporting, misreporting. Therefore, in view of this situation, this paper gives the design scheme of natural gas pipeline leakage diagnosis, and finally realizes the intelligent diagnosis of pipeline leakage with high accuracy. In this paper, the empirical mode decomposition (EMD) method can be used to decompose the original signal step by step according to different frequency scales, and the waves or trends of these different scales can be extracted to form the intrinsic mode function. Then the energy entropy and approximate entropy characteristics of each intrinsic modal function which can reflect the characteristics of the original signal are extracted. Due to the disadvantages of modal aliasing in traditional empirical mode decomposition (EMD) methods, the general empirical mode decomposition (EEMD) method and approximate entropy and energy entropy are used to extract the features. The feature vector groups extracted by four feature extraction methods are analyzed by using support vector machine (SVM), and the recognition effect is compared to judge the best feature extraction method. The complexity and generalization ability of SVM recognition and diagnosis method is determined by penalty factor C and kernel function parameter g. In order to improve the accuracy of recognition and diagnosis, an accurate, fast and stable method is needed to find the optimal parameters. In this paper, the Libsvm software platform is used to optimize the parameters of grid search, particle swarm optimization (PSO), genetic algorithm (GA) and the combination of particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the penalty factor C and kernel function parameter g of pipeline leakage type classification of support vector machine (SVM). The result of classification accuracy is compared to achieve the goal of high accuracy mode diagnosis.
【学位授予单位】：东北石油大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TE973.6
[Abstract]:Along with the natural gas strategic resource function and the status promotion, the transportation pipeline operation safety receives the people's attention more and more. When the pipeline is running and maintaining, it is necessary to carry on real-time monitoring, accurately judge the running condition of the pipeline, diagnose the hidden danger of leakage in time, and avoid the safety accident. Although pipeline leakage detection technology is constantly improving, but in pipeline leakage detection, there will still be misreporting, misreporting. Therefore, in view of this situation, this paper gives the design scheme of natural gas pipeline leakage diagnosis, and finally realizes the intelligent diagnosis of pipeline leakage with high accuracy. In this paper, the empirical mode decomposition (EMD) method can be used to decompose the original signal step by step according to different frequency scales, and the waves or trends of these different scales can be extracted to form the intrinsic mode function. Then the energy entropy and approximate entropy characteristics of each intrinsic modal function which can reflect the characteristics of the original signal are extracted. Due to the disadvantages of modal aliasing in traditional empirical mode decomposition (EMD) methods, the general empirical mode decomposition (EEMD) method and approximate entropy and energy entropy are used to extract the features. The feature vector groups extracted by four feature extraction methods are analyzed by using support vector machine (SVM), and the recognition effect is compared to judge the best feature extraction method. The complexity and generalization ability of SVM recognition and diagnosis method is determined by penalty factor C and kernel function parameter g. In order to improve the accuracy of recognition and diagnosis, an accurate, fast and stable method is needed to find the optimal parameters. In this paper, the Libsvm software platform is used to optimize the parameters of grid search, particle swarm optimization (PSO), genetic algorithm (GA) and the combination of particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the penalty factor C and kernel function parameter g of pipeline leakage type classification of support vector machine (SVM). The result of classification accuracy is compared to achieve the goal of high accuracy mode diagnosis.
【学位授予单位】：东北石油大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TE973.6
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