Analyze and process atomic clock difference data with Hilbert-Huang Transform

Fabric defect detection has been an active area of research since a long time and still a robust system is needed which can fulfill industrial requirements. A robust automatic fabric defect detection system (FDDS) would results in quality products and more revenues. Many different approaches and method have been tried to implement FDDS. This paper presents a new scheme for automated FDDS implementation using Hilbert-Huang Transform (HHT) and also compares it with O penCV approach. In my implementation of both approaches in same environment, the HHT approach produces higher defect detection accuracies than OpenCV approach and more computationally efficient. The article is divided into four parts, the first part is the introduction of t wo methods, the second part for the two methods defect detection system implementation, the third pa rt is the results and the discussion of two methods, the fourth part is the conclusion. HHT is used to deal with nonlinear and non-stationary signal, the method of its adaptive is good, can be used to analyze all kinds of signals. Compared with the Fourier transform, the HHT has more explicit time- frequency description; filter performance is even more acute. In addition, its implementation is simple and can be real-time calculation. Therefore, for the engineering application and theory research, HHT has very important significance. HHT method has two parts: Empirical mode decomposition and Hilbert spectrum analysis.

Near-infrared spectroscopy (NIRS) can be used as the basis of non-invasive neuroimaging that may allow the measurement of haemodynamic changes in the human brain evoked by applied stimuli. Since this technique is very sensitive, physiological interference arising from the cardiac cycle and breathing can significantly affect the signal quality. Such interference is difficult to remove by conventional techniques because it occurs not only in the extracerebral layer but also in the brain tissue itself. Previous work on this problem employing temporal filtering, spatial filtering, and adaptive filtering have exhibited good performance for recovering brain activity data in evoked response studies. However, in this study, we present a time-frequency adaptive method for physiological interference reduction based on the combination of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). Monte Carlo simulations based on a five-layered slab model of a human adult head were implemented to evaluate our methodology. We applied an EMD algorithm to decompose the NIRS time series derived from Monte Carlo simulations into a series of intrinsic mode functions (IMFs). In order to identify the IMFs associated with symmetric interference, the extracted components were then Hilbert transformed from which the instantaneous frequencies could be acquired. By reconstructing the NIRS signal by properly selecting IMFs, we determined that the evoked brain response is effectively filtered out with even higher signal-to-noise ratio (SNR). The results obtained demonstrated that EMD, combined with HSA, can effectively separate, identify and remove the contamination from the evoked brain response obtained with NIRS using a simple single source–detector pair.

Ring-shaped electrostatic gas-solid two-phase flow probe can provide mechanical parameters of the pneumatically transported powders, and has been used for measuring and controlling the pulverized coal flow distribution among conveyors leading to burners in coal-fired power stations. But the signal obtained from the probe demonstrates strongly non-linear characteristic. In this paper, efforts had been paid to investigate the signal by means of Hilbert-Huang transform, including the empirical mode decomposition (EMD) and Hilbert spectral analysis(HAS) on the signal of the probe. The EMD results unveiled that low-frequency oscillation existed and its amplitude was non-decayed. HAS interpreted the EMD results by time-frequency joint analysis and described the instantaneous behavior of gas-solid two-phase flow.

Conventional techniques for feature extraction are based on the stationary process and linear theory, so they are unsuited for processing the non-stationary signals. Hilbert-Huang transform (HHT) has been proposed recently and is specially fit to explore the non-stationary signals. The paper utilizes HHT to analyze the signals from the WZ12-1 platform. First, we employ a simulation signal to measure the performance of empirical mode decomposition (EMD), which is the core of HHT. Next, the HHT method is applied to extract the fault feature from the signals collected from the WZ12-1 platform and successfully reveals the reason causing the excessive vibration of the WZ12-1 platform. The results indicate that HHT could be a helpful method for the fault feature extraction of mechanical systems.Keywordsfeature extractionHilbert-Huang transform (HHT)empirical mode decomposition (EMD)offshore platform

Non-stationary and non-linear processes govern thermo-acoustic instability in a combustion or propulsion device, which involves coupling between the chamber acoustics and the heat-release oscillations. In this study, we demonstrate the efficacy of combining Empirical Mode Decomposition (EMD), Hilbert-Huang Transform (HHT), and non-linear time-series analysis to reveal the underlying oscillatory modes and the evolution of system dynamics. The experimental configuration consisted of a confined, partially premixed methane-air flame, in which the influence of swirl strength on the flame characteristics and naturally excited thermo-acoustic instabilities was investigated. The objective is to understand the swirl-flame combustion characteristics and the thermo-acoustic aspects of the system at different swirl intensities. The EMD-based analysis reveals that the flame characteristics, the temporal nature of the heat and pressure oscillations, and their coupling are closely linked to the strength of the swirling flow. For a fixed global equivalence ratio ( ϕ g ), the burner exhibited self-excited acoustic instability, accompanied by reduced combustion efficiency and a shorter flame standoff distance, at high swirl strengths. With changing swirl strength, the dynamics of the acoustic pressure changed from limit cycle oscillations and intermittency to a quasi-stable state. The study shows that analyzing thermo-acoustic instability using a combination of EMD and HHT can successfully isolate and reveal valuable information about the temporal features of the instability from non-stationary, non-linear acoustic pressure and heat release oscillation signals. The proposed EMD–Hilbert framework can be extended to practical systems, such as gas turbines and rocket combustors for early detection and characterization of thermo-acoustic instabilities.

One of the paramount goals in petroleum exploration is the identification of the reservoir. The foremost objective of reservoir characterization is for prediction of reservoir precisely. Nowadays, geophysical characterization is used to characterize a reservoir; mainly, seismic data is being used for understanding the reservoir’s properties and internal structure. The reservoir can cause anomalies in the frequency of seismic signals. Over the past few decades, various transforms such as Fourier transform (FT) and wavelet transform (WT) have been used to extract the concealed attributes in the data. In the case of the Fourier transform (FT), it is the global transform that cannot reflect local specialty. This transform is the best applicable for stationary and linear data, but most of the geophysical data are non-stationary, nonlinear, and aperiodic in nature. Therefore, FT is good for spectral analysis but not fit for elucidating the temporal characteristics of the data. Wavelet transform (WT) overcomes the problem faced by Fourier transform as it is one of the efficient techniques for both spectral and temporal analysis of the data. The main problem with WT is that it does not work well with nonlinear data and suitable mother wavelet is needed to be assumed. This study focuses on highlighting the anomalies using the Hilbert–Huang Transform, which comprises two techniques, i.e., empirical mode decomposition (EMD) technique and Hilbert spectral analysis (HSA). EMD will decompose a signal into mono-components of frequency termed as intrinsic mode functions (IMFs). In this study, the EMD technique followed by HSA is applied to the data set which highlights the anomalies in the data set and makes facile to characterize a reservoir. It is difficult to characterize a reservoir because of varying properties such as porosity and permeability within small sections of the reservoir. This method shows its effectiveness in characterizing a reservoir.

This paper explores the possibility of using a combination of the empirical mode decomposition (EMD) and the Hilbert transform (HT), termed the Hilbert-Huang transform (HHT) method, to identify the modal damping ratios of the structure with closely spaced modal frequencies. The principle of the HHT method and the procedure of using the HHT method for modal damping ratio identification are briefly introduced first. The dynamic response of a two-degrees-of-freedom (2DOF) system under an impact load is then computed for a wide range of dynamic properties from well-separated modal frequencies to very closely spaced modal frequencies. The natural frequencies and modal damping ratios identified by the HHT method are compared with the theoretical values and those identified using the fast Fourier transform (FFT) method. The results show that the HHT method is superior to the FFT method in the identification of modal damping ratios of the structure with closely spaced modes of vibration. Finally, a 36-storey shear building with a 4-storey light appendage, having closely spaced modal frequencies and subjected to an ambient ground motion, is analyzed. The modal damping ratios identified by the HHT method in conjunction with the random decrement technique (RDT) are much better than those obtained by the FFT method. The HHT method performing in the frequency-time domain seems to be a promising tool for system identification of civil engineering structures.

本文提出了基于希尔伯特–黄变换(Hilbert-Huang Transform, HHT)与Teager能量算子(Teager Energy Operator, TEO)相融合的谐振接地选线新方法。首先对零序电流信号进行经验模态分解法(Empirical Mode De-composition, EMD)分解，然后将TEO合理的引入选线算法中，并与HHT极性判别法相融合，实现了HHT与TEO两者融合的优势互补。通过仿真分析，验证了所提方法的可行性和有效性。 The combination method of Hilbert-Huang Transform (HHT) and Teager Energy Operator (TEO) for fault line selection with resonance grounding is proposed in this paper. The zero sequence current signals are decomposed using Empirical Mode Decomposition (EMD). The TEO is reasonable introduced into algorithm of fault line selection. The TEO is combined with the method of HHT polarity estimate. The complementary advantage for combination method of HHT and TEO is implemented. The feasibility and effectiveness of the proposed method is verified by simulation results.

The features extraction is the main step in a Brain-Computer Interface (BCI) design. Its goal is to create features easy to be interpreted in order to produce the most accurate control commands. For this end, these features must include all the original signal characteristics. The generated brain's signals' non-stationary and nonlinearity constitute a limitation to the improvement of the performances of systems based on traditional signal processing such as Fourier Transform. This work deals with the comparison of features extraction between Hilbert-Huang Transform (HHT) and Welch's method for Power Spectral Density estimation (PSD) then on the creation of an adaptive method combining the two. The parameters optimization of each method is firstly performed to reach the best classification accuracy rate. The study shows that the PSD estimation is sensitive to the parametric variation whereas the HHT method is mainly robust. The classification results show that an adaptive joint method can reach 90% of accuracy rate for a mental activity period of 1s.

Fluctuating pressure is an important feature of the bottom of a stilling basin with step-down floor. To analyze the frequency domain characteristics and energy distribution of this fluctuating pressure, the Hilbert–Huang transform (HHT) method is used. First, empirical mode decomposition is performed on the pressure fluctuation signal to obtain a number of intrinsic mode functions (IMFs), and then the Hilbert transformation is performed on each IMF to obtain the Hilbert spectrum and marginal spectrum for characterizing the pressure fluctuation signal. The results show that the fluctuating pressure signal of the stilling basin with step-down floor has obvious characteristics of low frequency and large amplitude. The dominant frequencies of the head and tail of the stilling basin are very prominent, and most of the energy is concentrated below 5.0 Hz; with the increase in the relative position of the measuring point, the energy distribution in stilling basin with step-down floor changes from high-frequency component to low-frequency component. The fluctuating pressure signal of the stilling basin with step-down floor has random amplitude modulation and frequency modulation. The marginal spectrum obtained by the HHT method can obtain the local characteristics of the signal more accurately and is more suitable for processing nonlinear and nonstationary signals.

Scramjet combustor states parameters dynamic characteristics study have great importance in scramjet accuracy thermodynamics regulations. In this investigation the dynamic response characteristics of kerosene-fueled scramjet combustor wall pressures are studied by time-frequency domain analysis method. Firstly, the dynamic response of scramjet combustor wall pressure is stimulated by step input of kerosene under different fuel equivalence ratio by using the direct-connected test facility. Secondly, the time-frequency spectrum of the dynamic response data are analyzed by using Hilbert-Huang transform (HHT) method. In HHT analysis, the raw experiment response data are filtered and decomposed into intrinsic mode functions (IMF) in time domain by the Empirical mode decomposition (EMD), then Hilbert spectral analysis (HSA) examine each IMF's instantaneous frequency as functions of time. Finally, time-frequency domain character distributions with different fuel equivalence ratio(ER) and wall pressure measurement location are given and analyzed by comparison study.

Fault diagnosis of gearbox using empirical mode decomposition and multi-fractal detrended cross-correlation analysis

Hilbert-Huang Transform (HHT) is a kind of signal analysis method suitable for non-stationary signal, and the combination of HHT and vector signal processing is called vector HHT, by which the instantaneous azimuth of the target can be estimated. In vector HHT signal processing method, the existence of intermittent signal and noise leads to mode mixing in the results of empirical mode decomposition (EMD). Mode mixing influences exact extraction of vibration mode, which causes that the signal which should be in the same order of intrinsic mode function (IMF) distributes in different IMFs. In vector HHT signal processing method, the two vibration speed components need to be multiplied by sound pressure respectively, so mode mixing will lead to great error in the estimation of azimuth of the target. To overcome the influence of mode mixing on vector HHT, this paper proposes a kind of vector HHT signal processing method based on scale filtering. The new method can effectively eliminate mode mixing resulting from intermittent signal and noise, and exact decomposition results can be achieved. The improved vector HHT method is used in processing acoustic vector signal, and the data obtained from a certain sea experiment in Dalian city is analyzed by the new method. The waterfall plot accurately shows the azimuth of the target ship, which proves the validity of the method.

This study introduces a novel pipeline for converting surface electromyography (sEMG) signals into muscle activations using the Hilbert-Huang Transform. Traditional approaches in this context often apply low-pass filters that suppress high-frequency components, potentially discarding physiologically relevant signal information. In contrast, the proposed method leverages Empirical Mode Decomposition and Hilbert spectral analysis to preserve the nonstationary and multi-frequency nature of sEMG data. Activation outputs are then mapped through physiologically inspired dynamics, yielding time-resolved muscle activations. Comparative analyses were conducted across three muscles (EDC, FDS, FDP) using data from 10 subjects each performing 5 cylindrical grasps. Intra-subject comparisons using Wilcoxon signed-rank tests revealed statistically significant improvements (p < 0.001) in nearly all trials. Linear mixed-effects analysis of log-transformed activations showed that the new pipeline yields significantly higher muscle activations within each muscle: EDC GMR = 1.31 (95% CI: 1.255–1.359), FDS GMR = 1.35 (95% CI: 1.296–1.396), and FDP GMR = 1.29 (95% CI: 1.248–1.342), all p < 0.001. These results suggest that the choice of sEMG processing pipeline can meaningfully alter activation estimates and potentially influence musculoskeletal model estimation. The method presented provides a robust and physiologically consistent alternative for applications in biomechanics, prosthetic control, and neuromuscular modelling.

This study analyses two different methods to detect obstructive sleep apnea (OSA) during sleep time based only on the ECG signal. OSA is a common sleep disorder caused by repetitive occlusions of the upper airways, which produces a characteristic pattern on the ECG. ECG features, such as the heart rate variability (HRV) and the QRS peak area, contain information suitable for making a fast, non-invasive and simple screening of sleep apnea. Fifty recordings freely available on Physionet have been included in this analysis, subdivided in a training and in a testing set. We investigated the possibility of using the recently proposed method of empirical mode decomposition (EMD) for this application, comparing the results with the ones obtained through the well-established wavelet analysis (WA). By these decomposition techniques, several features have been extracted from the ECG signal and complemented with a series of standard HRV time domain measures. The best performing feature subset, selected through a sequential feature selection (SFS) method, was used as the input of linear and quadratic discriminant classifiers. In this way we were able to classify the signals on a minute-by-minute basis as apneic or nonapneic with different best-subset sizes, obtaining an accuracy up to 89% with WA and 85% with EMD. Furthermore, 100% correct discrimination of apneic patients from normal subjects was achieved independently of the feature extractor. Finally, the same procedure was repeated by pooling features from standard HRV time domain, EMD and WA together in order to investigate if the two decomposition techniques could provide complementary features. The obtained accuracy was 89%, similarly to the one achieved using only Wavelet analysis as the feature extractor; however, some complementary features in EMD and WA are evident.