基于改进CEEMDAN-熵方法的管道泄漏工况识别

Identification of Pipeline Leakage Conditions Based on Improved CEEMDAN-Entropy

负压波信号的去噪效果和特征向量的提取是影响输油管道泄漏检测准确性的关键因素。针对当前管道泄漏检测准确性较低的问题,提出了改进的添加成对白噪声的完全集合经验模态分解算法(改进的CEEMDAN)对负压波信号进行预处理,将管道上下游压力传感器测得的负压波信号进行CEEMDAN分解,得到多个固有模态函数(IMF),并根据双通道传感器的相关系数原则筛选有效IMF分量,提出基于熵的特征向量,计算有效IMF分量的能量熵、峭度熵以及排列熵,并输入支持向量机(SVM)对不同工况进行分类。通过现场数据验证,改进的CEEMDAN-熵方法可以有效提高输油管道泄漏检测的准确性,具有一定的现场应用价值。

The denoising effect of the negative pressure wave signal and the extraction of the feature vector are the key factors affecting the accuracy of the oil pipeline leakage detection.Aiming at the false negatives and false positives in pipeline leak detection,this paper proposed an improved fully integrated empirical mode decomposition algorithm(improved CEEMDAN)with adaptive white noise to preprocess the negative pressure wave signal.The CEEMDAN decomposition is performed on the negative pressure wave signal measured by the upstream and downstream pressure sensors of the pipeline to obtain a plurality of intrinsic mode functions(IMF).And the effective IMF component is selected according to the correlation coefficient principle of the dual channel sensor.An entropy⁃based eigenvector is proposed,and the energy entropy,kurtosis entropy and permutation entropy of the effective IMF component are input to support vector machine(SVM)to distinguish different working conditions.Through field data verification,the improved CEEMDAN combined with the entropy⁃based feature vector can effectively improve the accuracy of oil pipeline leakage condition identification,and has certain field application value.