Micro-Vibration Signal Denoising Algorithm of Spectral Morphology Fitting Based on Variational Mode Decomposition

Abstract

Environmental micro-vibration has a significant impact on the proper functioning of semiconductor production and testing equipment such as the Czochralski growth furnace, polishing machine, photoetching machine, scanning electron microscope, etc. Low-frequency micro vibration has a significant influence on the normal operation of high-precision machining and testing equipment, and even causes irreversible damage to the equipment. Therefore, the micro-vibration test has important theoretical significance and engineering value for guiding the vibration isolation design of an electronic industrial workshop and ensuring the stable operation of various precision equipment in the workshop. As the observed acceleration signal is affected by noise introduced by the acceleration sensor itself, the signal processing circuit, the external power supply and interference from environmental factors, direct integration operations can lead to problems such as baseline drift and signal distortion in the calculation results. Aiming at the problem of noise interference in the micro-vibration measurement process, this paper proposed a micro-vibration signal denoising algorithm of spectral morphology fitting based on variational mode decomposition. The observed acceleration signal is decomposed into several orders of finite bandwidth intrinsic mode function components. The asymmetric Gaussian mixture model is used to complete the fitting of the spectral curves of each order of intrinsic mode function components. We can obtain the relevant parameters of the asymmetric Gaussian mixture model curves to complete the division of the effective information frequency band and finally achieve the denoising of the observed acceleration signal. Finally, the algorithm of this paper is compared with traditional denoising algorithms through numerical simulation examples and comparative experiments on denoising effects. The results show that the proposed algorithm has higher accuracy and anti-noise ability.