scholarly journals X-ray Pulsar Signal Denoising Based on Variational Mode Decomposition

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1181
Author(s):  
Qiang Chen ◽  
Yong Zhao ◽  
Lixia Yan
Keyword(s):  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
X Ray ◽  
Mode Decomposition ◽  
Control Research ◽  
Potential Applications ◽  
Initial Work ◽  
Navigation Control ◽  
Interplanetary Navigation ◽  
Filtering Effect

Pulsars, especially X-ray pulsars detectable for small-size detectors, are highly accurate natural clocks suggesting potential applications such as interplanetary navigation control. Due to various complex cosmic background noise, the original pulsar signals, namely photon sequences, observed by detectors have low signal-to-noise ratios (SNRs) that obstruct the practical uses. This paper presents the pulsar denoising strategy developed based on the variational mode decomposition (VMD) approach. It is actually the initial work of our interplanetary navigation control research. The original pulsar signals are decomposed into intrinsic mode functions (IMFs) via VMD, by which the Gaussian noise contaminating the pulsar signals can be attenuated because of the filtering effect during signal decomposition and reconstruction. Comparison experiments based on both simulation and HEASARC-archived X-ray pulsar signals are carried out to validate the effectiveness of the proposed pulsar denoising strategy.

scholarly journals Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3510 ◽  
Author(s):  
Zhijian Wang ◽  
Junyuan Wang ◽  
Wenhua Du
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Optimal Number ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Multiple Fault ◽  
Mode Decomposition ◽  
Fault Features ◽  
Reconstructed Signal

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

scholarly journals The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 359 ◽  
Author(s):  
Yuxing Li ◽  
Xiao Chen ◽  
Jing Yu ◽  
Xiaohui Yang ◽  
Huijun Yang
Keyword(s):  
Feature Extraction ◽  
Optimization Method ◽  
Sample Entropy ◽  
Maximum Energy ◽  
Data Driven ◽  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Radiated Noise ◽  
Mode Decomposition

The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode decomposition (IVMD). IVMD, as an improved method of variational mode decomposition (VMD), solved the problem of choosing decomposition layers for VMD by using a frequency-aided method. Furthermore, a novel method of feature extraction for S-RN, which combines IVMD and sample entropy (SE), is put forward in this paper. In this study, four types of S-RN signals are decomposed into a group of intrinsic mode functions (IMFs) by IVMD. Then, SEs of all IMFs are calculated. SEs are different in the maximum energy IMFs (EIMFs), thus, SE of the EIMF is seen as a novel feature for S-RN. To verify the effectiveness of the proposed method, a comparison has been conducted by comparing features of center frequency and SE of the EIMF by IVMD, empirical mode decomposition (EMD) and ensemble EMD (EEMD). The analysis results show that the feature of S-RN can be obtain efficiently and accurately by using the proposed method.

scholarly journals Application of variational mode decomposition to seismic random noise reduction

2017 ◽  
Vol 14 (4) ◽  
pp. 888-898 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Zhiming Wang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Random Noise ◽  
Computational Cost ◽  
Superior Performance ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Reconstruction Performance ◽  
Non Stationary Signal

Abstract We have proposed a new denoising method for the simultaneous noise reduction and preservation of seismic signals based on variational mode decomposition (VMD). VMD is a recently developed adaptive signal decomposition method and an advance in non-stationary signal analysis. It solves the mode-mixing and non-optimal reconstruction performance problems of empirical mode decomposition that have existed for a long time. By using VMD, a multi-component signal can be non-recursively decomposed into a series of quasi-orthogonal intrinsic mode functions (IMFs), each of which has a relatively local frequency range. Meanwhile, the signal will focus on a smaller number of obtained IMFs after decomposition, and thus the denoised result is able to be obtained by reconstructing these signal-dominant IMFs. Synthetic examples are given to demonstrate the effectiveness of the proposed approach and comparison is made with the complete ensemble empirical mode decomposition, which demonstrates that the VMD algorithm has lower computational cost and better random noise elimination performance. The application of on field seismic data further illustrates the superior performance of our method in both random noise attenuation and the recovery of seismic events.

scholarly journals Denoising of the Fiber Bragg Grating Deformation Spectrum Signal Using Variational Mode Decomposition Combined with Wavelet Thresholding

2019 ◽  
Vol 9 (1) ◽  
pp. 180 ◽  
Author(s):  
Weifang Zhang ◽  
Meng Zhang ◽  
Yan Zhao ◽  
Bo Jin ◽  
Wei Dai
Keyword(s):  
Discrete Wavelet ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Noisy Signals ◽  
Band Limited ◽  
Adaptive Signal Decomposition ◽  
Adaptive Signal ◽  
The Impact

Damage detection using an FBG sensor is a critical process for an assessment of any inspection technology classified as structural health monitoring (SHM). FBG signals containing noise in experiments are developed to detect flaws. In this paper, we propose a novel signal denoising method that combines variational mode decomposition (VMD) and changed thresholding wavelets to denoise experimental and mixed signals. VMD is a recently introduced adaptive signal decomposition algorithm. Compared with traditional empirical mode decomposition (EMD), and it is well founded theoretically and more robust to noise samples. First, input signals were broken down into a given number of K band-limited intrinsic mode functions (BLIMFs) by VMD. For the purpose of avoiding the impact of overbinning or underbinning on VMD denoising, the mixed signals, which were obtained by adding different signal/noise ratio (SNR) noises to the experimental signals, were designed to select the best decomposition number K and data-fidelity constraint parameter α. After that, the realistic experimental signals were processed using four denoising algorithms to evaluate denoising performance. The results show that, upon adding additional noisy signals and realistic signals, the proposed algorithm delivers excellent performance over the EMD-based denoising method and discrete wavelet transform filtering.

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Synthetic Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

scholarly journals Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

2019 ◽  
Vol 24 (2) ◽  
pp. 303-311 ◽  
Author(s):  
Xiaoxia Zheng ◽  
Guowang Zhou ◽  
Dongdong Li ◽  
Haohan Ren
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Bearing Vibration

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

Well-log decomposition using variational mode decomposition in assisting the sequence stratigraphy analysis of a conglomerate reservoir

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. B221-B228 ◽  
Author(s):  
Zhaohui Xu ◽  
Bo Zhang ◽  
Fangyu Li ◽  
Gang Cao ◽  
Yuming Liu
Keyword(s):  
Sequence Stratigraphy ◽  
Oil Field ◽  
Well Logs ◽  
Bohai Bay ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Sequence Identification ◽  
Band Limited ◽  
System Tracts

Sequence stratigraphy analysis is one of the most important tasks in evaluating and characterizing the reservoir system within a basin. However, it is very hard to identify the system tracts and lithofacies using well logs for the conglomerate reservoirs because of the strong lithology heterogeneity. Based on the fact that the system tracts and lithofacies usually illustrate cycle features within the basin, we decompose the well logs into different intrinsic modes to characterize the sequence units and lithofacies at different scale. First, we analyze the log response to lithologies to determine the well logs used for sequence analysis. Then, we use variational mode decomposition to decompose the selected well logs into an ensemble of different band-limited intrinsic mode functions, each with its center wavenumber. Finally, we interpret the sequence stratigraphy and lithofacies using corresponding decomposed modes. We validate the effectiveness of our method in the lithofacies and sequence identification for a conglomerate reservoir in the Shengli oil field, Bohai Bay Basin, east China. The decomposed intrinsic modes with a larger center wavenumber perfectly characterize the sequence units at a larger scale, whereas the decomposed intrinsic modes with a smaller center wavenumber reveal the lithofacies changes at a smaller scale. The application illustrates that it is much more convenient and easier for sequence stratigraphy analysis to integrate the original and decomposed logs.

scholarly journals An Efficient Correlation Power Analysis Attack Using Variational Mode Decomposition 

2020 ◽  
Vol 31 (1) ◽  
pp. 17-25
Keyword(s):  
Power Analysis ◽  
Side Channel ◽  
Variational Mode Decomposition ◽  
Secret Key ◽  
Intrinsic Mode Functions ◽  
Side Channel Attacks ◽  
Mode Decomposition ◽  
Noisy Conditions ◽  
Correlation Power Analysis ◽  
Evaluation Board

Side channel attacks (SCAs) are now a real threat to cryptographic devices and correlation power analysis (CPA) is the most powerful attack. So far, a CPA attack usually exploits the leakage information from raw power consumption traces that collected from the attack device. In real attack scenarios, these traces collected from measurement equipment are usually contaminated by noise resulting in a decrease in attack efficiency. In this paper, we propose a variant CPA attack that exploits the leakage information from intrinsic mode functions (IMFs) of the power traces. These IMFs are the results of the variational mode decomposition (VMD) process on the raw power traces. This attack technique decreases the number of power traces for correctly recovering the secret key by approximately 13% in normal conditions and 60% in noisy conditions compared to a traditional CPA attack. Experiments were performed on power traces of AES-128 implemented in both microcontroller and FPGA by Sakura-G/W side channel evaluation board to verify the effectiveness of our method.

scholarly journals A Novel Shearer Cutting State Recognition Method Based on Improved Variational Mode Decomposition and LSSVM with Acoustic Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Zhongbin Wang ◽  
Bin Liang ◽  
Lei Si ◽  
Kuangwei Tong ◽  
Chao Tan
Keyword(s):  
Search Algorithm ◽  
Optimal Parameter ◽  
Gravitational Search Algorithm ◽  
Principal Component ◽  
Least Square ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Recognition Method ◽  
Mode Decomposition

The recognition of shearer cutting state is the key technology to realize the intelligent control of the shearer, which has become a highly difficult subject concerned by the world. This paper takes the sound signal as analytic objects and proposes a novel recognition method based on the combination of variational mode decomposition (VMD), principal component analysis method (PCA), and least square support vector machine (LSSVM). VMD can decompose a signal into various modes by using calculus of variation and effectively avoid the false component and mode mixing problems. On this basis, an improved gravitational search algorithm (IGSA) is designed by using the position update mechanism of Levy flight strategy to find the optimal parameter combination of VMD. Then, the feature extraction is achieved by calculating the envelope entropy and kurtosis of the decomposed intrinsic mode functions (IMFs). To avoid dimensional disasters and reinforce the classification performance, PCA is introduced to choose useful features, and the LSSVM-based classifier is reasonably constructed. Finally, the experimental results indicate that the proposed method is more feasible and superior in the recognition of shearer cutting states.

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