在线压缩感知方法及其在漏磁检测中的应用

发布时间：2018-05-31 21:19

本文选题：压缩感知 + 漏磁检测　；参考：《仪器仪表学报》2017年07期

【摘要】：以长距离油管的漏磁检测系统为研究对象,研究了漏磁检测数据的在线压缩算法。针对嵌入式在线工作环境下,传统的数据压缩方法难以应用的问题,引入压缩感知(CS)理论,提出了漏磁检测数据在线CS压缩方法。确定了小波基作为漏磁信号的最佳稀疏表示基,并推导了小波稀疏基矩阵的数学表达公式;提出Welch界和PRP共轭梯度算法的测量矩阵优化算法;提出了漏磁检测数据的重要数据段筛选方法,极大地减少了数据存储量。仿真试验证明了所提出在线压缩算法极大地减少了在线环境压缩编码的运算复杂度,具有简单迅速、压缩比高、重构精度高等优点,符合漏磁检测数据在线压缩的实际要求。
[Abstract]:In this paper, the on-line compression algorithm of magnetic flux leakage (MFL) detection data for long distance tubing is studied. In order to solve the problem that the traditional data compression method is difficult to be applied in the embedded on-line working environment, a new on-line CS compression method for magnetic flux leakage detection data is proposed by introducing the theory of compression sensing (CSS). The wavelet basis is determined as the best sparse representation basis of magnetic flux leakage signal, and the mathematical expression formula of wavelet sparse basis matrix is derived, and the Welch bound and PRP conjugate gradient algorithm are proposed to optimize the measurement matrix. An important data segment screening method for magnetic flux leakage (MFL) detection data is proposed, which greatly reduces the data storage. The simulation results show that the proposed on-line compression algorithm greatly reduces the computational complexity of the compression coding in the online environment, and has the advantages of simplicity, high compression ratio and high reconstruction accuracy, and meets the practical requirements of on-line compression of magnetic flux leakage detection data.
【作者单位】：哈尔滨工程大学自动化学院;帝国理工学院;
【基金】：国家自然科学基金(51579047) 国家科技支撑计划课题(2013BAG25B01) 高等学校博士学科点专项科研基金(20132304120015) 黑龙江省博士后科研启动金(LBH-Q14040) MPRD专项支持(IEP-0401) 东南大学毫米波国家重点实验室开放课题(K201707) 中央高校基本科研业务费(HEUCF160414)项目资助
【分类号】：TE973.6

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