微弱裂纹信号的稀疏编码提取

Extraction of weak crack signals by sparse code

针对重大技术装备中关键基础部件早期裂纹信号提取困难这一问题,提出一种基于独立分量分析(ICA)的稀疏编码收缩(SCS)去噪方法,即采用泛化高斯模型(GGM)在ICA空间中估计信号独立系数的概率密度函数(PDF),并利用最大后验(MAP)估计方法进行非线性去噪的微弱信号提取方法。通过对不同信噪比的含噪微弱裂纹信号的提取研究,结果表明,此方法能提取出输入信噪比低于-27dB的微弱信号,且波形与频谱均能较好的和原信号保持一致。同时,其去噪效果远远好于小波降噪方法,是一种较好的微弱信号提取方法。

Aimed at the problem of hard extraction for early cracks in critical infrastructure components of major equipments, a sparse code shrinkage （SCS） denoising for weak signals based on independent component analysis （ICA） is proposed. Namely, the probability density function （PDF） of independent coefficients of the signal is estimated by the generalized Gaussian model （GGM） in the ICA space. And the nonlinear denoising is finished by maximum a posteriori （MAP） estimate. By extracting weak crack signals with differ- ent SNRs, the results show that this method can extract the signals with SNR less than --27dB. And the waveform and spectrum of the extracted signal are substantially consistent with the original one. At the same time, the results are much better than those from the wavelet denoising method. The method is very suitable for weak signal extraction.