Optimal signal reconstruction based on time-varying weighted empirical mode decomposition

Short-term traffic flow prediction is important to realize real-time traffic instruction. However, due to the existing strong nonlinearity and non-stationarity in short-term traffic volume data, it is hard to obtain a satisfactory result through the traditional method. To this end, this paper develops an innovative hybrid method based on the time varying filtering based empirical mode decomposition (TVF-EMD) and least square support vector machine (LSSVM). Specifically, TVF-EMD is firstly used to deal with the implied non-stationarity in the original data by decomposing them into several different subseries. Then, the LSSVM models are established for each subseries to capture the linear and nonlinear characteristics embedded in the original data, and the corresponding prediction results are superimposed to obtain the final one. Finally, case studies based on two groups of data measured from an arterial road intersection are employed to evaluate the performance of the proposed method. The experimental results indicate it outperforms the other involved models. For example, compared with the LSSVM model, the average improvements by the proposed method in terms of the indexes of mean absolute error, mean relative percentage error, root mean square error and root mean square relative error are 7.397, 15.832%, 10.707 and 24.471%, respectively.

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Detection of time varying pitch in tonal languages: an approach based on ensemble empirical mode decomposition

Journal of Zhejiang University SCIENCE C ◽

10.1631/jzus.c1100092 ◽

2012 ◽

Vol 13 (2) ◽

pp. 139-145 ◽

Cited By ~ 1

Author(s):

Hong Hong ◽

Xiao-hua Zhu ◽

Wei-min Su ◽

Run-tong Geng ◽

Xin-long Wang

Keyword(s):

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Time Varying ◽

Mode Decomposition ◽

Time Varying Pitch

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Fault Diagnosis of Rolling Bearing based on an Improved Denoising Technique using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method

10.21203/rs.3.rs-159429/v1 ◽

2021 ◽

Author(s):

Prashant Kumar Sahu ◽

Rajiv Nandan Rai

Keyword(s):

Empirical Mode Decomposition ◽

Signal Reconstruction ◽

Low Frequency ◽

Rolling Bearing ◽

Ensemble Empirical Mode Decomposition ◽

Adaptive Thresholding ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition ◽

Envelope Spectrum

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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Measurement While Drilling Mud Pulse Signal Denoising and Extraction Approach Based on Particle-Swarm-Optimized Time-Varying Filtering Empirical Mode Decomposition

SPE Drilling & Completion ◽

10.2118/204454-pa ◽

2020 ◽

pp. 1-11

Author(s):

Xianyou Zhong ◽

Li Cen ◽

Yankun Zhao ◽

Tianwei Huang ◽

Jinjin Shi

Keyword(s):

Particle Swarm Optimization ◽

Empirical Mode Decomposition ◽

Drilling Fluid ◽

Particle Swarm ◽

Pulse Signal ◽

Permutation Entropy ◽

Time Varying ◽

Swarm Optimization ◽

Mode Decomposition ◽

Reconstructed Signal

Summary At present, mud pulse transmission is widely used in underground wireless transmission. To extract more accurately the original drilling fluid pulse signals while drilling, in this paper, we developed an algorithm for optimal denoising shaping based on particle-swarm-optimized time-varying filtering empirical mode decomposition (TVFEMD). The performance of TVFEMD heavily depends on its parameters (i.e., B-spline order and bandwidth threshold). In the traditional TVFEMD method, the parameters are given in advance and may not be optimized, so it is difficult to achieve satisfactory decomposition results. To tackle this issue, the correlation coefficient was used as the objective function, and the particle-swarm-optimization algorithm was used to optimize the parameters of TVFEMD in this paper. First, the particle swarm optimization was used to search for the best combination of parameters. Then, the TVFEMD was applied to obtain a series of intrinsic mode functions (IMFs). Subsequently, the optimal denoising and shaping algorithm was used to determine the best reconstructed signal by low-pass filtering. Permutation entropy was taken as the evaluation index to obtain a reconstruction signal. Finally, the reconstructed signal was processed by square wave shaping to obtain accurate drilling fluid pulse signals. The approximation of the algorithm is 0.7581, and relevance is as high as 0.8535. The simulation signal and drilling fluid pulse signal analysis results showed that the proposed approach can extract the original pulse signal accurately.

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