Research on Signal Denoising Method of Self-propelled Gun Based on Improved Wavelet Threshold
To effectively filter out the noise in the vibration signal of self-propelled gun diesel engine,a vibration signal denoising method based on improved wavelet threshold was proposed.Firstly,the o-riginal vibration signals were processed by an improved complete ensemble empirical mode decomposi-tion with adaptive noise to obtain the intrinsic mode function components.Secondly,the randomness of the components was measured by multiscale permutation entropy to screen out components that need to be denoised.These selected components were denoised based on an improved wavelet threshold denoi-sing method.Finally,the required vibration signals were obtained by reconstructing denoised compo-nents and components without denoising.Meanwhile,in order to solve the problem that parameters in the multiscale permutation entropy manually selected have a significant influence on the calculation re-sults,an improved sparrow search algorithm was proposed to optimize the parameters in the multiscale permutation entropy.The feasibility and effectiveness of the proposed method was verified by simulated signals and measured data respectively.The results show that the proposed method can improve the sig-nal-to-noise ratio by 5.989 4 dB,6.078 7 dB and 1.565 3 dB respectively compared with wavelet threshold denoising,multiwavelet neighboring coefficient denoising and ICEEMDAN-MPE-wavelet threshold denoising when applied to simulated signal,and reduces the denoising error ratio by 22.1433,6.8349 and 0.722 7 respectively when applied to measured data.This method provides a new vibra-tion signal denoising idea for self-propelled gun.
万方数据
维普
