超声检测作为一种电力设备检测的方法，其回波常常受到噪声的干扰，为了提高检测的精准度，需要对回波信号进行去噪，以提高信号的质量。提出了一种基于自适应完备集合经验模态分解（CEEMDAN）与快速独立分量分析（FastICA）算法结合的去噪算法，应用于变压器套管引线的超声检测中。含噪信号经CEEMDAN算法分解成若干个模态分量（IMF），以满足独立分量分析对信号正定或超定要求，再用FastICA对IMF构建多维源信号，最后利用赫斯特（Hurst）指数阈值区分多维信号中的噪声，完成滤波并重构超声信号。通过仿真和试验得出结论：所提方法较其他算法，去噪后信噪比高，均值误差小，波形平滑性好，并且信号畸变程度低，能较好地保留回波的起振位置等有效信息，将套管内引线状态更好地提取出来，具有一定的实用意义。

Ultrasonic testing is a new way to detect power equipment. Its echo is often interfered by noise. In order to improve the accuracy of detection, it is necessary to denoise the echo signal and improve the signal quality. In this paper, a denoising algorithm based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEM) and fast independent component analysis (FastICA) is proposed. The noisy signal is decomposed into several modal components (IMF) by CEEMDAN algorithm to meet the requirements of blind source separation for signal positive or overdetermined, and then the multi-source signal is constructed by FastICA for IMF, and finally the Hurst index is used. The threshold distinguishes the noise in the multi-dimensional signal, completes the filtering and reconstructs the ultrasonic signal. Through simulation and experiment, this method can remove the noise signal better than other algorithms, retain the original information such as the starting position of the echo, and has higher signal-to-noise ratio, lower mean square error and the shortest running time. It can improve the accuracy of the ultrasonic detection sleeve lead state and has certain application value.