An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD‐ICA‐WTD

Zhang, Sunan; Tian, Jianyan; Banerjee, Amit; Li, Jiangli

doi:10.1155/2019/2858740

Cited by 6 publications

(4 citation statements)

References 35 publications

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“….,N, N is generally 100-300. After research [35], the added Gaussian white noise conforms to the following law:…”

Section: The Basic Principle Of Eemdmentioning

confidence: 99%

Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

Gao

et al. 2021

PLoS ONE

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Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD, and the main intrinsic modal components (IMF) are selected using comprehensive evaluation indicators; the second layer of filtering uses wavelet threshold denoising (WTD) to process the main IMF components. Finally, the virtual noise channel is introduced, and FastICA is used to de-noise and unmix the IMF components processed by the WTD. Next, perform spectral analysis on the separated useful signals to highlight the fault frequency. The feasibility of the proposed method is verified by simulation, and it is applied to the extraction of weak signals of faulty bearings and worn polycrystalline diamond compact bits. The analysis of vibration signals shows that this method can efficiently extract weak fault characteristic information of rotating machinery.

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“….,N, N is generally 100-300. After research [35], the added Gaussian white noise conforms to the following law:…”

Section: The Basic Principle Of Eemdmentioning

confidence: 99%

Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

Gao

et al. 2021

PLoS ONE

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“…Considering the computational efficiency, m � 3 is selected in this paper. Similar to PE [42], WPE can also be used to distinguish between signaldominant BLIMFs and noise-dominant BLIMFs. [4].…”

Section: Division Of Blimfs Based On Wpementioning

confidence: 99%

An Improved VMD-Based Denoising Method for Time Domain Load Signal Combining Wavelet with Singular Spectrum Analysis

Cai

Guo

et al. 2020

Mathematical Problems in Engineering

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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.

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“…Unlike the Fourier transform and wavelet transform, EEMD can decompose the original signal adaptively according to its characteristics. The noisy signal xðtÞ will be adaptively decomposed into m intrinsic modes IMFðtÞ and a residue r m ðtÞ from high to low frequency [23]; the decomposition process is described as follows:…”

Section: Improvement For the Variation Of Blade Tip Clearancementioning

confidence: 99%

High-Precision Extraction Method for Blade Tip-Timing Signal with Eddy Current Sensor

Zhang

Ding

Chen

2020

International Journal of Rotating Machinery

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Online monitoring of high-speed rotating blades has always been a hot topic. Of the various methods, the blade tip timing (BTT) technique, based on eddy current sensors, is considered to be the most promising. However, BTT signals are easily influenced by various factors, which means that the accurate extraction of BTT signals remains a challenge. To try to solve this problem, the causes of measurement error were analyzed. The three main reasons for the error were established: the variation in blade tip clearance, the interference of background noise, and the asymmetry of the blade tip shape. Further, pertinent improvement methods were proposed, and a compensation method was proposed for the errors caused by the variation of tip clearance. A new denoising and shaping algorithm based on ensemble empirical mode decomposition (EEMD) was introduced for the errors caused by background noise. Additionally, an optimal installation position of the sensor was also proposed for the errors caused by the asymmetry of the blade tip shape. Finally, simulations and experiments were used to demonstrate the improved methodology. The results show that the measurement error on vibration amplitude and vibration frequency using the proposed method is less than 2.89% and 0.17%, respectively, which is much lower than the traditional method (24.44% and 0.39%, respectively).

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An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD‐ICA‐WTD

Cited by 6 publications

References 35 publications

Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

An Improved VMD-Based Denoising Method for Time Domain Load Signal Combining Wavelet with Singular Spectrum Analysis

High-Precision Extraction Method for Blade Tip-Timing Signal with Eddy Current Sensor

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