Extraction Method of Characteristic Parameters of Magnetic Acoustic Emission Signals Based on CEEMD

Abstract

In view of the problem that magnetic acoustic emission (MAE) signal has strong background noise and characteristic parameters cannot be effectively extracted from the signal, the signal was decomposed by complementary empirical modal decomposition (CEEMD). The decomposed IMF components were analyzed with the original signal, and the IMF components with large correlation coefficients to the original signal were retained and added after the characteristic parameters are calculated. Therefore, the purpose of effectively suppressing noise can be achieved. A method for extracting MAE characteristic parameters based on CEEMD was proposed. The results of static tensile experiments show that the characteristic parameter diagram of the signal becomes smoother after CEEMD decomposition. Finally, the method was applied to the processing of MAE signal under low-cycle fatigue experiments. The results show that material yield and material hardening can be found in advance from the characteristic parameter diagram. The experimental results show that the proposed CEEMD algorithm has unique advantages in low-cycle fatigue MAE signal processing.