在线压缩感知方法及其在漏磁检测中的应用

On-line compressed sensing method and its application in magnetic flux leakage detection

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。

In this dissertation, taking the magnetic flux leakage （MFL） detection system for long-distance oil pipeline as study object, the online compression algorithm of magnetic flux leakage detection data is studied. Aiming at the problem that traditional data compression method is difficult to apply in the embedded online work environment, compressed sensing （CS） theory is introduced and an online CS compression method for MFL detection data is proposed. The wavelet base is determined as the best sparse representation base of the magnetic flux leakage signal, and the mathematical expression of the wavelet sparse base matrix is derived ; A measurement matrix optimization algorithm based on Welch bound and PRP conjugate gradient algorithm is proposed; An important data segment screening method of the MFL detection data is proposed, which greatly reduces the data storage size. The simulation resuhs show that the proposed online compression algorithm greatly reduces the computation complexity of compression encoding in online environment, has the advantages of simple and rapid operation, high compression ratio, high reconstruction precision and etc. , and meets the actual requirements of MFL detection data online compression.