ICEEMDAN-FE联合改进小波阈值的振动信号去噪算法OA

In view of the complex vibration signals and difficult noise removal in harsh environments,the improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and fuzzy entropy(FE)feature extraction are combined with the improved wavelet threshold,and a vibration signal denoising algorithm based on ICEEMDAN-FE and improved wavelet threshold is proposed.Firstly,the detected vibration signal is decomposed into multiple intrinsic mode functions(IMFs)and residuals with a relatively smooth trend by ICEEMDAN.Secondly,the FE feature extraction algorithm is used to calculate the FE eigenvalues of each IMF,and the IMF of the dominant part of the information is retained by the set IMF threshold conditions.Thirdly,the improved wavelet threshold is used to denoise the IMF of the retained dominant components of information.Finally,the residuals and the IMF after improved wavelet threshold denoising are subjected to signal reconstruction,so as to obtain the final signal.The filtering effect was evaluated by establishing simulation signals.The experimental results show that in comparison with ICEEMDAN denoising,wavelet threshold denoising and ICEEMDAN-wavelet threshold denoising,the signal-to-noise ratio(SNR)of the proposed algorithm is increased by 3.233 5 dB,1.181 1 dB and 1.066 3 dB,respectively,its normalized cross-correlation(NCC)increased by 0.033 42,0.009 39 and 0.008 4,respectively,and its root mean squared error(RMSE)decreased by 52.5%,23.81%and 21.77%,respectively.After importing the measured vibration signal,the denoising results also show that the effective signal is more complete and smoother,and the denoising effect is more ideal.