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利用独立分量分析的冗余取消特性,对多维加噪声观测信号进行盲源分离,得到源观测信号,实现噪声的有效消除,文章中应用此方法处理了仿真漏磁缺陷信号,实验结果表明:该除噪方法能极大提高漏磁信号的信噪比,且其效果要优于小波变换除噪方法。漏磁缺陷信号在时域上波形非常相似,很难加以分辨,而它们的危害性却大不相同,文章对裂纹和凹坑缺陷信号进行小波包分解,根据信号特征自适应的产生一组最优基来表征信号,分析这两种缺陷的时频特性,准确识别出这两种缺陷。
By using the redundant cancellation feature of independent component analysis, the multi-dimensional and noise-added observation signals are blind source separated to obtain the source observation signal, so as to effectively eliminate the noise. The method is applied to the simulation of the magnetic flux leakage defect signal. The experimental results show that: Noise removal method can greatly improve the signal to noise ratio of the magnetic flux leakage signal, and the effect is better than the wavelet transform denoising method. Magnetic flux leakage signal in the time domain waveform is very similar, it is difficult to be resolved, and their dangers are very different, the paper cracks and pit defect signal wavelet packet decomposition, according to the characteristics of the signal adaptive to produce a group of the most Excellent base to characterize the signal, analyze the time-frequency characteristics of these two defects, accurately identify these two defects.