希尔伯特-黄变换在脉冲涡流信号消噪与识别中的应用

摘要

针对传统方法无法有效识别不同尺寸细小裂纹所产生的脉冲涡流信号,提出一种基于希尔伯特-黄变换的脉冲涡流信号消噪与识别算法.对脉冲涡流信号进行集成经验模态分解并通过归一化自相关函数及其方差特性分选出含有噪声的本征模态函数;对含噪声的本征模态函数进行阈值消噪并与未做处理的本征模态函数重构成无噪声信号;对无噪声信号进行希尔伯特-黄变换并计算出希尔伯特边际谱;根据希尔伯特边际谱的差异识别出不同细小尺寸的表面与下表面裂纹.实验结果表明了所提方法的有效性,经过集成经验模态分解消噪,消除了噪声对脉冲涡流信号的干扰;而基于希尔伯特-黄变换的方法则能够有效识别出不同尺寸的裂纹.%A de-noising and recognition method based on HHT ( Hilbert-Huang transform ) was proposed to solve the problem that the traditional methods cannot effectively identify the pulse eddy current signals produced by small defects with different sizes.Firstly, the pulse eddy current signal was decomposed by EEMD (ensemble empirical mode decomposition), and the IMFs (intrinsic mode functions) of much noise were selected according to normalized autocorrelation function and its variance.Secondly, the selected IMFs of much noise were removed by the wavelet threshold de-noising, and then the noiseless signal was reconstructed by adding to the non-processed IMFs.Then, the HMS ( Hilbert marginal spectrum) was obtained by using HHT.Finally, according to the difference of HMS, the surface and subsurface defects with different sizes were identified.Experimental results show the effectiveness of the proposed method: the noise of pulsed eddy current signal is eliminated by noise elimination through EEMD, and the method based on HHT can effectively identify cracks of different sizes.