Optimal Signal Reconstruction Using the Empirical Mode Decomposition

Abstract The vibration signals for rotating machines are generally polluted by excessive noise and can lose the fault information at the early development phase. In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using CEEMD algorithm. The IMFs grouping and selection are formed based upon the correlation coefficient value. The noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction. The effectiveness of the proposed method denoised signals are measured based on kurtosis value and the envelope spectrum analysis. The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing.

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Denoising of MEMS Vector Hydrophone Signal Based on Empirical Model Wavelet Method

Proceedings ◽

10.3390/proceedings2019015011 ◽

2019 ◽

Vol 15 (1) ◽

pp. 11 ◽

Cited By ~ 1

Author(s):

Huichao Yan ◽

Linmei Zhang

Keyword(s):

Empirical Mode Decomposition ◽

Signal Reconstruction ◽

Acoustic Signals ◽

Spectral Energy ◽

Detection Accuracy ◽

Denoising Method ◽

Reconstruction Process ◽

Underwater Acoustic ◽

Mode Decomposition ◽

Vector Hydrophone

Underwater acoustic technology is a major method in current ocean research and exploration, which support the detection of seabed environment and marine life. However, the detection accuracy is directly affected by the quality of underwater acoustic signals collected by hydrophones. Hydrophones are efficient and important tools for collecting underwater acoustic signals. The collected signals of hydrophone often contain lots kinds of noise as the work environment is unknown and complex. Traditional signal denoising methods, such as wavelet analysis and empirical mode decomposition, product unsatisfied results of denoising. In this paper, a denoising method combining wavelet threshold processing and empirical mode decomposition is proposed, and correlation analysis is added in the signal reconstruction process. Finally, the experiment proves that the proposed denoising method has a better denoising performance. With the employment of the proposed method, the underwater acoustic signals turn smoothly and the signal drift of the collected hydroacoustic signal is improved. Comparing the signal spectrums of other methods, the spectral energy of the proposed denoising method is more concentrated, and almost no energy attenuation occurred.

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A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

Entropy ◽

10.3390/e22091039 ◽

2020 ◽

Vol 22 (9) ◽

pp. 1039 ◽

Cited By ~ 1

Author(s):

Haikun Shang ◽

Yucai Li ◽

Junyan Xu ◽

Bing Qi ◽

Jinliang Yin

Keyword(s):

Empirical Mode Decomposition ◽

Signal Reconstruction ◽

Partial Discharge ◽

Hybrid Approach ◽

Approximate Entropy ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Fusion Algorithm ◽

Mode Decomposition ◽

Adaptive Noise

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

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Walsh Transform and Empirical Mode Decomposition Applied to Reconstruction of Velocity and Displacement from Seismic Acceleration Measurement

Applied Sciences ◽

10.3390/app10103509 ◽

2020 ◽

Vol 10 (10) ◽

pp. 3509 ◽

Cited By ~ 1

Author(s):

Qi Zhang ◽

Xiang Yuan Zheng

Keyword(s):

Empirical Mode Decomposition ◽

Signal Reconstruction ◽

Vibration Signal ◽

Walsh Transform ◽

Ocean Engineering ◽

Seismic Acceleration ◽

Acceleration Signal ◽

Mode Decomposition ◽

Least Squares Fit ◽

Reconstructed Signal

This paper focuses on reconstruction of dynamic velocity and displacement from seismic acceleration signal. For conventional time-domain approaches or frequency-domain approaches, due to initial values and non-negligible noise in the acceleration signal, drift and deviation in velocity and displacement are inevitable. To deal with this deficiency, this paper develops a Walsh transform and Empirical Mode Decomposition (EMD)-based integral algorithm, or WATEBI in short. In the WATEBI algorithm, the Walsh transform is employed to realize vibration signal reconstruction. Next, the EMD method is used to eliminate the residual in the reconstructed signal. Finally, the trend term in velocity and displacement is removed by linear least-squares fit. This algorithm can be straightforwardly implemented by an ordinary computer. Reconstructed displacements and velocities from vibration of a simulated single-degree-of-freedom system and two-site measured ground motions in earthquakes validated the robustness and adaptiveness of this algorithm. It can be also applied to many other areas, like mechanical engineering and ocean engineering.

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