Abstract
Measured load data play a crucial role in the fatigue durability analysis of mechanical structures. However, in the process of signal acquisition, time domain load signals are easily contaminated by noise. In this paper, a signal denoising method based on variational mode decomposition (VMD), wavelet threshold denoising (WTD), and singular spectrum analysis (SSA) is proposed. Firstly, a simple criterion based on mutual information entropy (MIE) is designed to select the proper mode number for VMD. Detrended fluctuation analysis (DFA) is adopted to obtain the noise level of the noisy signal, which can optimize the selection of MIE threshold. Meanwhile, the noisy signal is adaptively decomposed into band-limited intrinsic mode functions (BLIMFs) by using VMD. In addition, weighted-permutation entropy (WPE) is applied to divide the BLIMFs into signal-dominant BLIMFs and noise-dominant BLIMFs. Then, the signal-dominant BLIMFs are reconstructed with the noise-dominant BLIMFs processed by WTD. Finally, SSA is implemented for the reconstructed signal. Experimental results of synthetic signals demonstrate that the presented method outperforms the conventional digital signal denoising methods and the related methods proposed recently. Effectiveness of the proposed method is verified through experiments of the measured load signals.
Highlights
Fatigue failure is one of the main causes of mechanical failure. us, it is of great significance to analyze the fatigue durability of the mechanical structure [1]. e basis of durability design of agricultural vehicles is to obtain time domain load signals, which can reflect the real working condition
It can be seen that compared with the other methods, variational mode decomposition (VMD)-wavelet threshold denoising (WTD)-singular spectrum analysis (SSA) has higher SNRout and lower root mean square error (RMSE) and maximum absolute error (MAE). erefore, the results show that the VMDWTD-SSA proposed in this paper has the best denoising performances with different SNRin values. e presented method is better than the other methods on the whole, and its performance on noisy signals with low signal-to-noise ratio (SNR) is more prominent
In order to improve the denoising performance of time domain load signals, an efficient denoising method based on VMD, WTD, and SSA is proposed in this paper
Summary
Fatigue failure is one of the main causes of mechanical failure. us, it is of great significance to analyze the fatigue durability of the mechanical structure [1]. e basis of durability design of agricultural vehicles is to obtain time domain load signals, which can reflect the real working condition. Erefore, it is of great significance to effectively remove the noise in time domain load signals for the durability analysis of agricultural machinery. Erefore, the parameter values are usually determined based on experience and convenience, which seriously affects the denoising performance of VMD. For this reason, researchers have proposed many algorithms to select the mode number. Some scholars have proposed to use the wavelet threshold method to remove noise components from high-frequency components and reconstruct the signal [27]. DFA is used to get the noise level of the noisy signal, and mutual information entropy (MIE) is used to determine the mode number for adaptive VMD denoising.
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