在钢丝绳无损检测领域，漏磁检测是最成熟也是应用最广泛的一种方法，但受检测环境影响，其难以直接从漏磁信号中提取缺陷特征。为此提出了一种基于经验模态分解的缺陷检测方法，首先使用小波和自适应调整阈值的软阈值方法去除漏磁信号中的噪声分量；然后采用经验模态分解方法分解信号，提取其中微弱的有用信息；最后，结合中等相关及以上的分量对信号进行重构，从而提取损伤特征，确定了损伤类型和位置。所提方法实现了钢丝绳在安全可用范围内的最大化利用，具有良好的经济效益。

Wire rope is more and more widely used in industrial production. Its safety has become increasingly prominent. In the field of wire rope nondestructive testing, magnetic flux leakage testing is the most mature and widely used method. However, it is difficult to extract defect features directly from the signals of magnetic flux leakage testing due to the influence of testing environment. Therefore, a defect detection method based on empirical mode decomposition is proposed to detect defects. Firstly, the noise component in magnetic flux leakage signal is removed via wavelet and adaptive soft threshold method. Then, the obtained signal is decomposed by empirical mode decomposition, and the weakly related components are further processed by the wavelet method to extract the weak useful information. Finally, the signal is reconstructed by combining the weak information with the moderately related and above components, thus extracting the damage characteristics and determining damage types and damage locations. The proposed method achieves the maximum utilization of the wire rope in the safety available range with a good economic benefits.