EMD-based time–frequency denoising algorithm for the self-sensing of vibration signals in ultrasonic-assisted grinding

Abstract

An empirical mode decomposition (EMD) based time-frequency denoising algorithm is derived in this paper to accomplish the extraction of active ingredients for the multi-frequency mixed signals, and then the self-sensing of vibration signals is realized without additional sensors for the ultrasonic assisted grinding device. The EMD is employed to accomplish the signal decomposition, and multiple intrinsic mode function (IMF) components and the residual are obtained. Then, the weighted factors used for signal reconstruction are obtained based on innovation statistical distance, which is selected as a criterion to evaluate the time-frequency domain correlation between decomposition results and the original signal. Next, the maximum correntropy criterion (MCC) based similarity detection method is designed to adaptively modify the weighted factors, and the signal is reconstructed on the foundation of the modified weighted factors. Finally, numerical simulations and self-sensing experiment are conducted to verify the denoising performance of the proposed algorithm. The self-sensing of 4.59 dB vibration signal is realized for the ultrasonic assisted grinding device.

ICEEMDAN/LOESS: An Improved Vibration-Signal Analysis Method for Marine Atomic Interferometric Gravimetry

“…Tang [14] used the morphological singular value decomposition filtering noise-reduction method to denoise the vibration signal, then used the EMD algorithm to extract the fault features in the signals, and the experimental results verified that the approach could effectively obtain the bearing fault feature information. Meanwhile, Zhan [15] derived an EMD-based time-frequency denoising algorithm to accomplish the extraction of the efficient components of the multi-frequency mixed signal, realized the self-sensing of the vibration signal of ultrasonic-assisted grinding equipment, and verified the noise-reduction performance of the proposed algorithm through numerical simulations and self-sensing experiments. Zhong [16] used an improved ensemble empirical mode decomposition (EEMD) and depth belief network (DBN) approach to extract bearing speed features and perform vibration analyses.…”

PSO-optimized SSLMS adaptive filter for signal denoising of rolling bearings under small sample condition

“…Common denoising algorithms include empirical mode decomposition (EMD) [17][18][19], variational mode decomposition (VMD) [20][21][22], wavelet transform (WT) [23][24][25], and additional techniques. While these techniques demonstrate positive results in denoising, numerous challenges remain that necessitate resolution.…”

A new rapid positioning strategy for the impact-point using dynamic mode decomposition and time difference of arrival *

“…In the process of seismic wave signal analysis, researchers have proposed numerous filtering methods, such as Kalman [29,30] filtering, wavelet transforms filtering [31,32], SVD [33,34], EMD [35][36][37][38][39] and so on. DMD is an emerging data dimensionality reduction method that can also be utilized for analyzing seismic wave signals.…”