Seismic signal denoising using thresholded variational mode decomposition

Li, Fangyu; Zhang, Bo; Verma, Sumit; Marfurt, Kurt J.

doi:10.1071/eg17004

Cited by 82 publications

(29 citation statements)

References 23 publications

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“…When 0 < α < 0.5, the signal is anticorrelated. When α is in the range between 0.5 and 1.0, temporal correlations exist [36]. DFA has become a widely used technique for determination of scaling properties and detection of long-range correlations in non-stationary time series owing to its reliable ability of detrending the time series [37].…”

Section: B Detrended Fluctuation Analysismentioning

confidence: 99%

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A Novel Fault Diagnosis Approach for Rolling Bearing Based on High-Order Synchrosqueezing Transform and Detrended Fluctuation Analysis

2020

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Time-frequency analysis always plays an important role in machine health monitoring owing to its advantage in extracting the fault information contained in non-stationary signal. In this paper, we present a novel technique to detect and diagnose the rolling bearing faults based on high-order synchrosqueezing transform (FSSTH) and detrended fluctuation analysis (DFA). With this method, the high-order synchrosqueezing transform is first utilized to decompose the vibration signal into an ensemble of oscillatory components termed as intrinsic mode functions (IMFs). Meanwhile, an empirical equation, which is based on the DFA, is introduced to adaptively determine the number of IMFs from FSSTH. Then, a timefrequency representation originated from the decomposed modes or corresponding envelopes is exhibited in order to identify the fault characteristic frequencies related to rolling bearing. Experiments are carried out using both simulated signal and real ones from Case Western Reserve University. Results show that the proposed method is more effective for the detection of fault characteristic frequencies compared with the traditional synchrosqueezing transform (SST) based fault diagnosis algorithm, which renders this technique is promising for machine fault diagnosis. INDEX TERMS Fault diagnosis, rolling bearing, time-frequency analysis, high-order synchrosqueezing transform, detrended fluctuation analysis.

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Section: B Detrended Fluctuation Analysismentioning

confidence: 99%

“…To make the FSSTH adaptive, we need to determine the optimal number of IMFs (K ) in a data-driven way. In the paper, we utilize an empirical equation for the determination of K [36]:…”

Section: Simulation Testmentioning

confidence: 99%

A Novel Fault Diagnosis Approach for Rolling Bearing Based on High-Order Synchrosqueezing Transform and Detrended Fluctuation Analysis

2020

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“…To make the FSSTH adaptive, we need to determine the optimal number of IMFs (K) in a data-driven way. In the paper, we utilize an empirical equation for the determination of K [39]:…”

Section: A Evaluation On Simulated Ecg Signalmentioning

confidence: 99%

Noise Reduction in ECG Signal Using an Effective Hybrid Scheme

et al. 2020

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Electrocardiogram (ECG) is a critical biological signal, which usually carries a great deal of essential information about patients. The high quality ECG signals are always required for a proper diagnosis of cardiac disorders. However, the raw ECG signals are highly noisy in nature. In the paper, we propose a hybrid denoising scheme to enhance ECG signals by combining high-order synchrosqueezing transform (FSSTH) with non-local means (NLM). With this method, a noisy ECG signal is first decomposed into an ensemble of intrinsic mode functions (IMFs) by FSSTH. Then, some noise is removed by eliminating a set of noisy IMFs that are determined by a scaling exponent obtained by the detrended fluctuation analysis (DFA); while the remaining IMFs are filtered by NLM. Finally, the denoised ECG signal is obtained by reconstructing the processed IMFs. Experiments are carried out using the simulated ECG signals and real ones from the MIT-BIH database, and the denoising performances are evaluated in terms of signal to noise ratio (SNR), root mean squared error (RMSE) and percent root mean square difference (PRD). Results show that the hybrid denoising scheme involving both FSSTH and NLM is able to suppress complex noise from ECG signals more effectively while preserving the details well. INDEX TERMS Electrocardiogram signal, noise reduction, high-order synchrosqueezing transform, detrended fluctuation analysis, non-local means.

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“…Colominas et al proposed a new improved CEEMD; the new method was used for artificial signals and real biomedical signals [19]. The improvements of the CEEMD method have been achieved through the application of correlation theory [20][21][22][23][24]. The main steps of the CEEMD are as follows:…”

Section: The Proposed Methodsmentioning

confidence: 99%

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Huang

Malekian

et al. 2017

Energies

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Hilbert-Huang transform (HHT) is a popular method to analyze nonlinear and non-stationary data. It has been widely used in geophysical prospecting. This paper analyzes the mode mixing problems of empirical mode decomposition (EMD) and introduces the noncontact measurement and detection of instantaneous seismic attributes using complementary ensemble empirical mode decomposition (CEEMD). Numerical simulation testing indicates that the CEEMD can effectively solve the mode mixing problems of EMD and can provide stronger anti-noise ability. The decomposed results of the synthetic seismic record show that CEEMD has a better ability to decompose seismic signals. Then, CEEMD is applied to extract instantaneous seismic attributes of 3D seismic data in a real-world coal mine in Inner Mongolia, China. The detection results demonstrate that instantaneous seismic attributes extracted by CEEMD are helpful to effectively identify the undulations of the top interfaces of limestone.

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Seismic signal denoising using thresholded variational mode decomposition

Cited by 82 publications

References 23 publications

A Novel Fault Diagnosis Approach for Rolling Bearing Based on High-Order Synchrosqueezing Transform and Detrended Fluctuation Analysis

A Novel Fault Diagnosis Approach for Rolling Bearing Based on High-Order Synchrosqueezing Transform and Detrended Fluctuation Analysis

Noise Reduction in ECG Signal Using an Effective Hybrid Scheme

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

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