## 1 Introduction
Eliminating trend term error by wavelet transform has strict requirements on the selection of wavelet basis and the determination of decomposition depth. Compared with the previous methods, the empirical mode decomposition method does not consider the trend term type and is more adaptive [19]. The variational mode decomposition algorithm (VMD) is introduced in this paper to avoid problems such as endpoint effect, over-envelope, under-envelope and false component in the iterative process of the traditional empirical mode decomposition method [20]. The  
Figure 1: Vibrational velocity spectrum with low frequency noise. algorithm can decompose a signal into a set of finite bandwidth intrinsic mode function components. Compared with the traditional empirical mode decomposition method, the VMD algorithm is more robust to noise. It is found that although the time-domain integration method is more intuitive in the form of expression, the denoising effect of the time-domain integration method is not ideal when the complex noise is aliased in the acceleration signal. Therefore, in engineering practice, the frequency domain integration method is widely employed to complete micro-vibration signal denoising, among which the frequency cutoff algorithm, low frequency attenuation algorithm and effective frequency band selection algorithm are more typical. As the trend term error is mostly presented as low-frequency and slowly changing signals, the frequency cutoff algorithm [21] and low-frequency attenuation algorithm [22] both select the upper frequency of the trend term error and then nullify the frequency information between the zero frequency and the upper frequency (frequency cutoff) or attenuation suppression (low-frequency attenuation). Both algorithms have significant denoising effects for trend term errors, but the processing effect is not ideal for the noise randomly distributed in other frequency bands. To solve this problem, [PERSON] [23] proposed an acceleration integration method based on spectral energy morphology fitting. He used Gaussian curve to fit the spectrum curve of the frequency band near the main frequency in the spectrum curve and determined the effective frequency band range of the main frequency according to the principle of triple standard deviation to achieve the denoising effect. However, there are some problems in the symmetric Gaussian curve spectrum shape fitting denoising algorithm, such as artificially dividing the peak acceleration interval, the great influence of noise outside the main frequency, the poor flexibility of the algorithm and the lack of effective information of the main frequency signal. Aiming at the problem of micro-vibration measurement in the vibration-sensitive area of the electronic industrial workshop, this paper accurately measured the micro-vibration in the vibration-sensitive area to guide the vibration isolation design. To address the problem that trend term errors and random noise interfere with micro-vibration measurements, this paper proposed a micro-vibration signal denoising algorithm of spectral morphology fitting based on variational mode decomposition. The spectral curve of the effective frequency band is fitted by the asymmetric Gaussian mixture model curve and finally complete the denoising of the vibration signal. The algorithm can effectively suppress the trend term error and random noise interference, and better integration accuracy and anti-noise ability for asymmetric acceleration spectrum.