基于CEEMDAN-HD-云模型特征熵的油气管道工况识别

针对长输油气管道泄漏检测过程中泄漏信号特征信息提取困难,提出一种新的管道负压波信号特征提取方法.采用添加自适应噪声的完备集合经验模态分解算法对采集的负压波信号进行去噪,通过评估CEEMDAN分解后分量与原始信号的概率密度之间的豪斯多夫距离选取有效模态并重构.计算重构信号的云模型特征熵、峭度作为特征参数,用支持向量机进行...

Identification of Oil and Gas Pipeline Working Condition Based on CEEMDAN -HD- Cloud Model Feature Entropy

Aiming at the difficulty in extracting the feature information of the leakage signal in the process of long-distance oil and gas pipeline leakage detection, a new pipeline negative pressure wave signal feature extraction method is proposed. A complete set of empirical mode decomposition algorithm with adaptive noise is used to denoise the collected negative pressure wave signal, and the Hausdorff distance between the probability density of the component after CEEMDAN decomposition and the original signal is evaluated. Select the effective mode and reconstruct. The cloud model feature entropy and kurtosis of the reconstructed signal are calculated as feature parameters, and the support vector machine is used for classification and recognition. Through laboratory data verification, the method of combining CEEMDAN, Hausdorff distance and cloud model feature entropy can effectively improve the accuracy of oil and gas pipeline leak detection, and realize the identification of small leak signals with a flow rate of less than 4^3m/h. Certain field application value.