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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。
Taking the long-distance magnetic flux leakage detection system as the research object, the on-line compression algorithm of the magnetic flux leakage detection data was studied. Aiming at the problem that the traditional data compression method is difficult to be applied under embedded online working environment, CS theory is introduced and an on-line CS compression method of magnetic flux leakage testing data is proposed. The wavelet basis is defined as the best sparse representation of MFL signal, and the mathematical expression of wavelet sparse basis matrix is deduced. The optimization algorithm of the measurement matrix of Welch bound and PRP conjugate gradient algorithm is proposed. The importance of magnetic flux leakage testing data Data segment screening method, greatly reducing the amount of data storage. The simulation results show that the proposed online compression algorithm greatly reduces the computational complexity of online environment compression coding. It has the advantages of simple and rapid, high compression ratio, high reconstruction accuracy and meets the actual requirements of on-line compression of magnetic flux leakage detection data.