摘要
针对油气管道泄漏检测过程中,泄漏信号包含大量噪音、特征提取困难等问题,提出一种改进的总体平均经验模态分解联合卡尔曼滤波算法的管道信号去噪方法。首先采用改进的总体平均经验模态算法对采集到的管道负压波信号进行分解,其中利用排列熵和卡尔曼滤波算法对分解后的固有模态分量进行筛选和处理,最后得到重构后的削噪信号。并且提出基于散布熵和峭度的特征提取法,将提取的特征参数作为支持向量机的输入来对输油管道的工况进行分类识别。经采集到的数据验证,改进的总体平均经验模态分解、卡尔曼滤波、散布熵与峭度结合的组合识别方法可以较准确的对管道信号进行分类识别,结果显示其总平均识别准确率达到98.89%,为管道的工况识别研究提供了一种新的途径。
In the process of oil and gas pipeline leak detection, the leak signal contains a lot of noise and the feature extraction is difficult. An improved total average empirical mode decomposition combined with Kalman filter algorithm is proposed to denoise the pipeline signal. First, the improved overall average empirical mode algorithm is used to decompose the collected pipeline negative pressure wave signal. The permutation entropy and Kalman filter algorithm are used to filter and process the decomposed inherent modal components, and finally the reconstructed Cut the noise signal. Furthermore, a feature extraction method based on diffusion entropy and kurtosis is proposed, the extracted feature parameters are used as the input of support vector machine to classify and recognize the working conditions of oil pipelines. The collected data verify that the improved overall average empirical mode decomposition, Kalman filter, spread entropy and kurtosis combined recognition method can more accurately classify and recognize pipeline signals, and the results show that the total average recognition accuracy is 98.89%, it provides a new way for the research of pipeline working condition identification.
作者
张勇
周兴达
王明吉
杨文武
刘洁
韦焱文
Zhang Yong;Zhou Xingda;Wang Mingji;Yang Wenwu;Liu Jie;Wei Yanwen(School of Physics and Electronic Engineering,Northeast Petroleum University,Daqing 163318,China;Artificial Intelligence Energy Research Institute,Northeast Petroleum University,Daqing 163318,China)
出处
《电子测量技术》
北大核心
2022年第11期64-71,共8页
Electronic Measurement Technology
基金
国家自然科学基金(61873058)
教育部重点实验室开放基金(MECOF2019B02)项目资助。
