Steel wire rope defect magnetic flux leakage detection method based on improved complementary ensemble empirical mode decomposition

The signal of small defects in steel wire rope is often submerged in wave noise. Therefore, it is difficult to detect small defects in wire rope and easy to miss detection. In order to solve this problem, a steel wire rope defect magnetic flux leakage detection method based on improved complementary ensemble empirical mode decomposition (ICEEMD) is proposed. To avoid the influence of the lubricant or dust on the surface of the wire rope on the detection signal, the electromagnetic detection method is adopted. CEEMD-WTF-WF multi-stage noise reduction method is obtained by combining ICEEMD, wavelet threshold filtering (WTF) and Wiener filtering (WF). The intrinsic mode function (IMF) component is obtained by decomposing the magnetic flux leakage signal of steel wire rope through ICEEMD. The energy ratio, permutation entropy and cross-correlation coefficient of IMF components are calculated. The IMF trend component and IMF stock noise component are extracted. WTF is conducted on the stock noise component to filter the useful IMF component reconstruction signal. WF is applied to the reconstructed signal to remove random noise. The eigenvalues of the de-noised defects are extracted, input and trained by BP neural network. The magnetic flux leakage signals of the steel wire rope defects are identified. The experimental results show that ICEEMD-WTF-WF multi-stage noise reduction method has good noise reduction effect on the magnetic flux leakage signal of steel wire rope. The SNR and kurtosis indexes are better than those of WTF, moving average filter and WF. The BP neural network model based on ICEEMD-WTF-WF takes a short time to detect. The average accuracy rate of small defects reaches 98.13%, which can better meet the requirements of wire rope defect detection.