Analysis of natural mineral earthquake and blast based on Hilbert–Huang transform (HHT)

Li, Xuelong; Li, Zhonghui; Wang, Enyuan; Feng, Junjun; Kong, Xiangguo; Chen, Liang; Li, Baolin; Li, Nan

doi:10.1016/j.jappgeo.2016.03.024

Cited by 54 publications

(20 citation statements)

References 29 publications

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“…We can also further get the MS of the signal by the method proposed in [16]. HHT is designed to work for data that is non-stationary and nonlinear [17], such as the propagation of seismic waves [18,19] and detection of earthquake’s hypocenter [20,21]. …”

Section: Related Workmentioning

confidence: 99%

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

Liu

et al. 2016

Sensors

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Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

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Section: Related Workmentioning

confidence: 99%

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

Liu

et al. 2016

Sensors

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“…It is important to notice that the two important tools (i.e., EMD and HS) for exploratory analysis of the data are provided by HSA method. This approach was applied successfully in various research fields as fault diagnosis for rolling bearing [11], turbulence [38], environment [34,39], and geophysics [40], to cite a few.…”

Section: Hilbert Spectrummentioning

confidence: 99%

Spectral Observations of PM10 Fluctuations in the Hilbert Space

Plocoste¹,

Calif²

2020

Functional Calculus

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During the last 20 years, many megacities have experienced air pollution leading to negative impacts on human health. In the Caribbean region, air quality is widely affected by African dust which causes several diseases, particularly, respiratory diseases. This is why it is crucial to improve the understanding of PM10 fluctuations in order to elaborate strategies and construct tools to predict dust events. A first step consists to characterize the dynamical properties of PM10 fluctuations, for instance, to highlight possible scaling in PM10 density power spectrum. For that, the scale-invariant properties of PM10 daily time series during 6 years are investigated through the theoretical Hilbert frame. Thereafter, the Hilbert spectrum in time-frequency domain is considered. The choice of theoretical frame must be relevant. A comparative analysis is also provided between the results achieved in the Hilbert and Fourier spaces.

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“…Thereby, the Hilbert transform can be used to obtain the Hilbert and marginal spectrum distributions [30]. The Hilbert transform is defined as,…”

Section: Hilbert Transformmentioning

confidence: 99%

The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection

Chen¹,

Shen²,

Chen³

et al. 2017

Applied Sciences

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Abstract:Grinding chatter reduces the long-term reliability of grinding machines. Detecting the negative effects of chatter requires improved chatter detection techniques. The vibration signals collected from grinders are mainly nonstationary, nonlinear and multidimensional. Hence, bivariate empirical mode decomposition (BEMD) has been investigated as a multiple signal processing method. In this paper, a feature vector extraction method based on BEMD and Hilbert transform was applied to the problem of grinding chatter. The effectiveness of this method was tested and validated with a simulated chatter signal produced by a vibration signal generator. The extraction criterion of true intrinsic mode functions (IMFs) was also investigated, as well as a method for selecting the most ideal number of projection directions using the BEMD algorithm. Moreover, real-time variance and instantaneous energy were employed as chatter feature vectors for improving the prediction of chatter. Furthermore, the combination of BEMD and Hilbert transform was validated by experimental data collected from a computer numerical control (CNC) guideway grinder. The results reveal the good behavior of BEMD in terms of processing nonstationary and nonlinear signals, and indicating the synchronous characteristics of multiple signals. Extracted chatter feature vectors were demonstrated to be reliable predictors of early grinding chatter.

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Analysis of natural mineral earthquake and blast based on Hilbert–Huang transform (HHT)

Cited by 54 publications

References 29 publications

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

Spectral Observations of PM10 Fluctuations in the Hilbert Space

The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection

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