Bearing Acoustic Emission Signal Processing based on Improved SGMD

Abstract

To solve the problems of poor adaptability to fixed thresholds and obvious endpoint effect in traditional Symplectic Geometric Mode Decomposition (SGMD), an improved SGMD (ISGMD) algorithm is proposed to improve the acoustic emission signal processing performance in bearing fault diagnosis. Firstly, a dynamic threshold adjustment model was constructed by fusing the signal Lyapunov exponent and the fractal dimension to realize the adaptive decomposition of signals of different complexity. Secondly, combined with the improved mirror extension and cosine smoothing technology, the endpoint effect is suppressed and the waveform distortion is avoided. Furthermore, the composite multi-scale dispersive entropy (CMSD) is introduced to screen the effective components, and the multi-scale entropy value is used to quantify the signal characteristics, eliminate the noise and reconstruct the components with high information order. Experiments show that ISGMD has excellent decomposition effect in noisy simulation signals, and the decomposition does not produce modal aliasing. In the actual bearing fault signal analysis, the reconstructed signal retains the outstanding peak characteristics, and the noise component is removed to a certain extent. This method significantly improves the robustness of signal decomposition and the ability to extract fault features, and provides an effective tool for the diagnosis of AE bearings under complex working conditions.