【摘要】：針對天然氣管道泄漏檢測過程中難以提取泄漏特征信息及泄漏定位精度低的問題,本文將局部均值分解(Local Mean Decomposition,LMD)算法應用于管道泄漏檢測中,實現管道泄漏信號的分解、特征提取以及泄漏定位。首先,介紹了局部均值分解的理論算法,并將其應用在信號分解中。LMD作為處理非平穩(wěn)隨機信號的一種有效手段,其具備信號分解的自適應特性和完備性。但由于算法本身的影響,易產生模態(tài)混疊。針對模態(tài)混疊現象,本文利用總體局部均值分解算法(Ensemble Local Mean Decomposition,ELMD),借助輔助噪聲技術,抑制LMD分解過程中模態(tài)混疊的問題。其次,有用信息在傳輸過程中往往會受到各種噪聲的影響和干擾,使信號源中的有用信號被削減或混淆。為增強有用信號,抑制噪聲干擾,保障后續(xù)提取的特征值能夠代表信號特征,需要對采集的原始信號進行降噪預處理。同時為避免小波分解過程存在的技術漏洞,提出基于小波包的ELMD譜峭度聯合降噪算法。該算法在ELMD分解出的各有效PF(Product Function)分量的基礎上,利用譜峭度最優(yōu)參數及小波包能量分布確定信號重構節(jié)點,完成對各PF分量的信號降噪。降噪后的各PF分量可表征原始信號在不同尺度下的特征。再次,通過分析管道信號的特點,對時頻分析理論進行研究,提出基于時頻域的自適應最優(yōu)核(Adaptive Optimal Kernel,AOK)譜熵參數,定量描述信號的時頻特性。對各PF分量中提取相應AOK參數用于初步判定管道是否發(fā)生泄漏及工況種類,其針對管道正常運行、管道泄漏、管道敲擊具有很好的區(qū)分度,具有良好的識別準確率。最后,介紹了基于ELMD多尺度相關的管道泄漏檢測算法。通過經ELMD分解得到的PF分量與互相關算法的融合,得到不同特征尺度下的時延差,從而完成對管道泄漏的定位。該算法相比直接利用原始信號進行相關計算得到的定位結果更加精確,有助于管道泄漏定位精度的提升。
[Abstract]:In view of the difficulty of extracting leakage characteristic information and the low accuracy of leak location in the process of natural gas pipeline leakage detection, this paper applies the local mean decomposition (Local Mean Decomposition,LMD) algorithm to pipeline leakage detection to realize the decomposition of pipeline leakage signal. Feature extraction and leak location. Firstly, this paper introduces the theoretical algorithm of local mean decomposition, and applies it to signal decomposition. As an effective method to deal with non-stationary random signals, LMD has the adaptive property and completeness of signal decomposition. However, due to the influence of the algorithm itself, it is easy to produce modal aliasing. Aiming at the phenomenon of modal aliasing, the problem of modal aliasing in the process of LMD decomposition is suppressed by means of the total local mean decomposition algorithm (Ensemble Local Mean Decomposition,ELMD) and the auxiliary noise technique. Secondly, the useful information is often affected and interfered by various noises in the transmission process, which reduces or confuses the useful signals in the signal source. In order to enhance the useful signal suppress the noise interference and ensure that the extracted eigenvalue can represent the signal feature it is necessary to pre-process the original signal. In order to avoid the technical loophole in the wavelet decomposition process, a ELMD spectral kurtosis joint denoising algorithm based on wavelet packet is proposed. On the basis of the effective PF (Product Function) components decomposed by ELMD, the optimal parameters of spectral kurtosis and the energy distribution of wavelet packets are used to determine the reconstructed nodes of the signal, and the signal denoising of each PF component is completed. Each PF component after denoising can characterize the characteristics of the original signal at different scales. Thirdly, by analyzing the characteristics of pipeline signals, the time-frequency analysis theory is studied, and an adaptive optimal kernel (Adaptive Optimal Kernel,AOK spectral entropy parameter based on time-frequency domain is proposed to quantitatively describe the time-frequency characteristics of the signals. The corresponding AOK parameters are extracted from each PF component to determine whether there is leakage or not and the working conditions of the pipeline are preliminarily determined. It has a good degree of distinction and good recognition accuracy for the normal operation of the pipeline, pipeline leakage and pipe percussion. Finally, the pipeline leak detection algorithm based on ELMD multi-scale correlation is introduced. Through the fusion of the PF component obtained by ELMD decomposition and the cross-correlation algorithm, the delay difference of different characteristic scales is obtained, and the location of pipeline leakage is completed. The algorithm is more accurate than that obtained by correlation calculation using the original signal directly, and it is helpful to improve the location accuracy of pipeline leakage.
【學位授予單位】：東北石油大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN911.7

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本文編號：2310549