管道泄漏模拟声发射的压缩感知及经验模态分解

Compressed Sensing and Empirical Mode Decomposition of a Simulated Acoustic Emission Signal of Pipeline Leakage

管道泄漏监测中常用到声发射信号检测技术。压缩感知理论是一种高效的信号采集压缩处理方法,将其应用到模拟声发射信号的采样重构中,可以使信号采样不再受Nyquist采样定理的限制,降低了数据采集成本,通过重构算法实现对原始信号的精确重构。进而对重构声发射信号进行分解,通过对比信号的经验模态分解,集合经验模态分解和掩膜信号法分解结果,表明掩膜信号法能有效抑制分解过程中存在的模态混叠现象,使分解结果更加精确有效。为声发射信号的特征提取打下坚实基础。

Acoustic emission（AE） signal detection technique is used in pipeline leakage monitoring constantly. Compressed sensing（CS） theory is a kind of efficient method of signal acquisition and compression. CS is applied to the reconstruction and sampling of simulated AE signal successfully without the restriction of the signal Nyquist sampling theorem. The costs of data acquisition are reduced, and the reconstruction of the original signal is realized by the reconstruction algorithm. The reconstructed signal is decomposed. By comparing the method of empirical mode decomposition, ensemble empirical mode decomposition and the masking signal decomposition, it shows that the masking signal method can effectively restrain the aliasing in modal decomposition process. The results of decomposition are more accurate and effective. The whole process lays a solid foundation for acoustic emission signal feature extraction.