Transcranial ultrasound detection of intracranial hemorrhages: time-frequency analysis with empirical and variational mode decomposition

2017 ◽  
Vol 141 (5) ◽  
pp. 3956-3956 ◽  
Author(s):  
Michael P. Jordan ◽  
Amber B. Bennoui ◽  
Anjelica A. Molnar-Fenton ◽  
Phillip J. White
Keyword(s):  
Frequency Analysis ◽  
Transcranial Ultrasound ◽  
Variational Mode Decomposition ◽  
Intracranial Hemorrhages ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

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 Time-Frequency Analysis Based on Improved Variational Mode Decomposition and Teager Energy Operator for Rotor System Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Shangkun Liu ◽  
Guiji Tang ◽  
Xiaolong Wang ◽  
Yuling He
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Energy Operator ◽  
Turbine Rotor ◽  
Variational Mode Decomposition ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Multicomponent Signal ◽  
Mode Decomposition ◽  
Teager Energy

A time-frequency analysis method based on improved variational mode decomposition and Teager energy operator (IVMD-TEO) is proposed for fault diagnosis of turbine rotor. Variational mode decomposition (VMD) can decompose a multicomponent signal into a number of band-limited monocomponent signals and can effectively avoid model mixing. To determine the number of monocomponents adaptively, VMD is improved using the correlation coefficient criterion. Firstly, IVMD algorithm is used to decompose a multicomponent signal into a number of monocompositions adaptively. Second, all the monocomponent signals’ instantaneous amplitude and instantaneous frequency are demodulated via TEO, respectively, because TEO has fast and high precision demodulation advantages to monocomponent signal. Finally, the time-frequency representation of original signal is exhibited by superposing the time-frequency representations of all the monocomponents. The analysis results of simulation signal and experimental turbine rotor in rising speed condition demonstrate that the proposed method has perfect multicomponent signal decomposition capacity and good time-frequency expression. It is a promising time-frequency analysis method for rotor fault diagnosis.

Modal Analysis of a Bridge During High-Speed Train Passages by Enhanced Variational Mode Decomposition

2020 ◽  
Vol 20 (13) ◽  
pp. 2041002
Author(s):  
Xiao-Mei Yang ◽  
Chun-Xu Qu ◽  
Ting-Hua Yi ◽  
Hong-Nan Li ◽  
Hua Liu
Keyword(s):  
Energy Distribution ◽  
Modal Analysis ◽  
Frequency Analysis ◽  
High Speed ◽  
Large Scale ◽  
Variational Mode Decomposition ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Variations

Modal analysis of bridge under high-speed trains is essential to the design and health monitoring of bridge, but it is difficult to be implemented since the vehicle–bridge interaction (VBI) effect is involved. In this paper, the time–frequency analysis technique is performed on the non-stationary train-induced bridge responses to estimate the frequency variations. To suppress the interference terms in time–frequency analysis but preserve the time-variant characteristics of responses, the enhanced variational mode decomposition (VMD) is proposed, which is used to decompose the train-induced dynamic response into many of envelope-normalized intrinsic mode functions (IMFs). Then the short-time Fourier transform is applied to observe the time–frequency energy distribution of each IMF. The train-induced bridge signals measured from a large-scale high-speed railway bridge are analyzed in this paper. The IMFs associated with the pseudo-frequencies caused by train or the resonant frequencies of bridge are distinguished. And, frequency variations are captured from the time–frequency energy distributions of envelope-normalized IMFs. The results show the proposed method can extract the frequency variations of low-energy IMFs effectively, which are hard to be observed from the time–frequency energy distribution of train-induced bridge response. The instantaneous frequency characteristics extracted from the train-induced bridge response could be the important support for investigating the VBI effect of train–bridge system.

scholarly journals Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

2020 ◽  
Vol 10 (5) ◽  
pp. 1696 ◽  
Author(s):  
Xiaoan Yan ◽  
Ying Liu ◽  
Wan Zhang ◽  
Minping Jia ◽  
Xianbo Wang
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Simulated Data ◽  
Energy Operator ◽  
Analysis Tool ◽  
Variational Mode Decomposition ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Compound Signal

Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD has a certain endpoint effect phenomenon. Hence, to overcome these drawbacks, this paper presents a novel time-frequency analysis algorithm termed as improved adaptive variational mode decomposition (IAVMD) for rotor fault diagnosis. First, a waveform matching extension is employed to preprocess the left and right boundaries of the raw compound signal instead of mirroring the extreme extension. Then, a grey wolf optimization (GWO) algorithm is employed to determine the inside parameters ( α ^ , K) of VMD, where the minimization of the mean of weighted sparseness kurtosis (WSK) is regarded as the optimized target. Meanwhile, VMD with the optimized parameters is used to decompose the preprocessed signal into several mono-component signals. Finally, a Teager energy operator (TEO) with a favorable demodulation performance is conducted to efficiently estimate the instantaneous characteristics of each mono-component signal, which is aimed at obtaining the ultimate time-frequency representation (TFR). The efficacy of the presented approach is verified by applying the simulated data and experimental rotor vibration data. The results indicate that our approach can provide a precise diagnosis result, and it exhibits the patterns of time-varying frequency more explicitly than some existing congeneric methods do (e.g., local mean decomposition (LMD), empirical mode decomposition (EMD) and wavelet transform (WT) ).

scholarly journals Time-Frequency Analysis and Application of a Vibration Signal of Tunnel Excavation Blasting Based on CEEMD-MPE-HT

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chen-yang Ma ◽  
Li Wu ◽  
Miao Sun ◽  
Qing Yuan
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Band ◽  
Frequency Analysis ◽  
Frequency Characteristic ◽  
Permutation Entropy ◽  
Natural Vibration Frequency ◽  
Characteristic Parameters ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

The traditional empirical mode decomposition method cannot accurately extract the time-frequency characteristic parameters contained in the noisy seismic monitoring signals. In this paper, the time-frequency analysis model of CEEMD-MPE-HT is established by introducing the multiscale permutation entropy (MPE), combining with the optimized empirical mode decomposition (CEEMD) and Hilbert transform (HT). The accuracy of the model is verified by the simulation signal mixed with noise. Based on the project of Loushan two-to-four in situ expansion tunnel, a CEEMD-MPE-HT model is used to extract and analyze the time-frequency characteristic parameters of blasting seismic signals. The results show that the energy of the seismic wave signal is mainly concentrated in the frequency band above 100 Hz, while the natural vibration frequency of the adjacent existing tunnel is far less than this frequency band, and the excavation blasting of the tunnel will not cause the resonance of the adjacent existing tunnel.

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