小波包多级树模型管道泄漏信号压缩感知方法

发布时间：2018-01-18 03:21

本文关键词：小波包多级树模型管道泄漏信号压缩感知方法　出处：《仪器仪表学报》2014年03期 　论文类型：期刊论文

【摘要】：针对目前管道泄漏监测信号压缩感知处理过程中,所需测量数较高,在数据压缩比较高的情况下泄漏信号重构性能指标比较低,未对稀疏信号固有的结构特征充分考虑的问题,提出了基于小波包多级树模型的管道泄漏监测信号稀疏化表示方法和结构模型的快速贝叶斯匹配追踪算法(M-FBMP)的压缩感知方法,该方法充分利用管道泄漏监测信号的特点合理选择稀疏基,构建了一次分解小波包多级树的管道泄漏监测信号模型,优化了标准高斯测量矩阵,推导了基于管道泄漏监测信号稀疏信号结构的M-FBMP算法,实现了信号的压缩采样和重构。通过实验对比该方法与传统快速贝叶斯匹配追踪(FBMP)压缩感知方法,实验给出了不同数据压缩比条件下,2种压缩感知方法所能达到的性能指标,结果表明所提方法与基于FBMP的压缩感知方法在均方根误差、信号的重构信噪比相同量级的情况下,数据压缩比提高了3.4倍,大大降低了工业监控数据传输对网络通信信道带宽的要求。在数据压缩比为30∶1的条件下,利用所提算法进行信号恢复,信号的重构信噪比提高了近2倍;重构能量恢复系数优于1×10-3,重构信号精度可以满足管道泄漏检测与定位的要求。
[Abstract]:In the process of pipeline leakage monitoring signal compression and sensing processing, the number of measurement is high, and the performance index of leakage signal reconstruction is low when the data compression is high. The structural characteristics of sparse signals are not fully considered. A sparse representation method of pipeline leakage monitoring signals based on wavelet packet multilevel tree model and a fast Bayesian matching tracking algorithm (M-FBMP) based on structural model are proposed in this paper. This method makes full use of the characteristics of pipeline leakage monitoring signal to select sparse base reasonably, constructs the model of pipeline leakage monitoring signal which decomposes wavelet packet multilevel tree, and optimizes the standard Gao Si measurement matrix. The M-FBMP algorithm based on sparse signal structure of pipeline leakage monitoring signal is derived. The compression sampling and reconstruction of the signal are realized. By comparing the method with the traditional fast Bayesian matching tracking (FBMP) compression sensing method, the different data compression ratios are given in the experiment. The results show that the proposed method and the compression sensing method based on FBMP have the same order of magnitude of RMS error and signal reconstruction SNR. The data compression ratio is increased by 3.4 times, which greatly reduces the requirement of network communication channel bandwidth for industrial monitoring data transmission. Under the condition of data compression ratio of 30: 1, the proposed algorithm is used for signal recovery. The signal-to-noise ratio (SNR) of the reconstructed signal is increased by nearly 2 times. The reconstruction energy recovery coefficient is better than 1 脳 10 ~ (-3), and the precision of reconstructed signal can meet the requirements of pipeline leakage detection and location.
【作者单位】：北京化工大学信息科学与技术学院;
【基金】：中央高校基本科研业务费项目(ZZ1226)资助
【分类号】：TN911.7
【正文快照】： 1引言管道运输是石油和天然气的主要运输方式,随着以微处理器为基础的远程终端装置等在整个工业监控网络的普遍应用,管道泄漏远程监测已经成为保证油气管道安全运行的主要措施之一。目前监控网络规模不断扩大,输油站及监控点不断增加,管道监测控制中心通过远程终端装置、数据

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