Instantaneous Envelope Research Articles

A Ground-Penetrating Radar-Based Study of the Structure and Moisture Content of Complex Reconfigured Soils

To increase the detection accuracy of soil structure and moisture content in reconstituted soils under complex conditions, this study utilizes a 400 MHz ground-penetrating radar (GPR) to examine a study area consisting of loess, sandy loam, red clay, and mixed soil. The research involves analyzing the single-channel waveforms and two-dimensional images of GPR, preprocessing the data, obtaining envelope information via amplitude envelope detection, and performing a Hilbert transformation. This study employs a least squares fitting approach to the instantaneous phase envelope to ascertain the thickness of various soil layers. By utilizing the average envelope amplitude (AEA) method, a correlation between the radar’s early signal amplitude envelope and the soil’s shallow dielectric constant is established to invert the moisture content of the soil. The analysis integrates soil structure and moisture distribution data to investigate soil structure characteristics and moisture content performance under diverse soil properties and depths. The findings indicate that the envelope detection method effectively identifies stratification boundaries across different soil types; the AEA method is particularly efficacious in inverting the moisture content of reconstituted soils up to 3 m deep, with an average relative error ranging from 2.81% to 7.41%. Notably, moisture content variations in stratified reconstituted soils are more pronounced than those in mixed soil areas, displaying a marked stepwise increase with depth. The moisture content trends in the vertical direction of the same soil profile are generally consistent. This research offers a novel approach to studying reconstituted soils under complex conditions, confirming the viability of the envelope detection and AEA methods for intricate soil investigations and broadening the application spectrum of GPR in soil studies.

Uncertainty assessment in unsupervised machine-learning methods for deepwater channel seismic facies using outcrop-derived 3D models and synthetic seismic data

Unsupervised machine-learning (ML) techniques have been widely applied to analyze seismic reflection data, including the identification of seismic facies and structural features. However, interpreting the resulting clusters often relies on geoscientists’ expertise, necessitating a robustness assessment of these methods. To evaluate their reliability, synthetic data generated from an actual outcrop model are used to demonstrate how two unsupervised methods, self-organizing maps (SOMs) and generative topographic maps (GTMs), cluster deepwater channel-related seismic facies and then measure the associated error. Six seismic attributes, comprising root-mean-square amplitude, instantaneous envelope, peak magnitude, and spectral decomposition frequencies at 20, 40, and 55 Hz, served as input variables. Geobodies are assigned to each cluster formed, and the error in facies clustering is quantified by comparing the actual 3D model with the facies grouped by ML methods on a voxel-by-voxel basis. This allowed for error quantification and the computation of metrics such as F1 score and accuracy through correlation matrices. Key findings reveal that (1) GTM and SOM exhibit similar performance, with a clustering configuration of 81 for GTM slightly outperforming others. (2) Error rates are approximately 2% for the predominant facies (background shale) but significantly higher for individual channel-related facies, suggesting that channel clusters might represent multiple facies. (3) Resolution and imbalanced data distribution impact seismic facies predictability, resulting in nonuniqueness in cluster generation. (4) Using synthetic seismic data proved valuable for experimenting with different unsupervised MLs, highlighting the need for assessing uncertainty in these methods, given their implications for crucial economic decisions reliant on reservoir interpretation, modeling, and volumetric estimations.

Application and Research of Microseismic Monitoring System and Hydraulic Fracturing Technology in Coal Mines

In order to improve the effectiveness of coal mine gas control and enhance the level of coal mine safety production, the application of a microseismic monitoring system and hydraulic fracturing technology in coal mines was studied. Applying hydraulic fracturing technology to coal mine gas treatment, firstly, the geological structure and gas concentration in the mining area are detected using the radio tunnel perspective method and infrared differential absorption method. Then, the relevant parameters of hydraulic fracturing are determined, and finally, hydraulic fracturing technology is implemented. Microseismic monitoring technology is used to monitor the cracks formed during hydraulic fracturing construction and evaluate the fracturing effect. The instantaneous energy envelope is obtained from the microseismic data of each detection channel after stacking and Hilbert transform static correction. A microseismic in-phase inversion positioning objective function based on travel time residuals is constructed, and under the constraints of polarization analysis, the optimal solution is obtained through search iteration to complete microseismic in-phase inversion positioning. Experimental results have shown that after applying this method to coal mine gas control, the gas concentration decreases below the execution standard, achieving good control effects. Under microseismic monitoring in coal mines, the hydraulic fracturing effect can be effectively and reasonably evaluated, and the safety production level of coal mines can be improved.

Identification of MAG welding metal droplet transfer patterns based on features of input power

An experimental platform was built for simultaneous acquisition of the input and output electrical signals as well as droplet transfer images during the welding process. By analyzing the rectifier circuit of the welder, the calculation method of the instantaneous input power, the instantaneous input power envelope, and the input pulsating energy (EP) were given. The input pulsating energy was found to better characterize the dynamical change process at the output terminal upon comparison. Then, the short circuiting transfer, free transfer and mixed transfer patterns were obtained by varying the wire feeding speed and the given voltage. Based on the standard variance (SD) and coefficient of variation of the input pulsating energy (v(Ep)), the parameter variations in different transition patterns and the stability of the transition process were compared and analyzed. The results reveal that the v(Ep) was greater than 18% for short circuiting transfer and less than 9% for free transfer.

Safe operating envelope based on a single-track model for yaw instability avoidance of articulated heavy vehicles

The electrification of commercial vehicles has led to new wheel torque allocation options for propulsion and braking of articulated heavy vehicles. Each unit or axle can be individually braked or propelled whilst the other units or axles are excluded from such action. This may achieve the best energy efficiency. However, this can also lead to potential yaw stability problems such as jackknifing and trailer swing, especially under bad loading and weather conditions. This paper describes the above instabilities and introduces a nonlinear single-track model to study the vehicle dynamics of the tractor-semitrailer combination. The effects of different vehicle and environment parameters are analysed with this vehicle model. A safe operating envelope for limiting the wheel forces is obtained using this vehicle model. Since the vehicle model introduced in this paper is computationally effective, it can be run online in real vehicles, with an instantaneous safe operating envelope obtained for the momentary conditions. Thus, yaw instabilities such as jackknifing and trailer swing may be avoided.

ЗАСТОСУВАННЯ ДІАГРАМИ КІНЕМАТИЧНОГО ГВИНТА ПРИ ФОРМУВАННІ КРИВОЛІНІЙНИХ ПОВЕРХОНЬ В ОЗБРОЄННІ ТА ВІЙСЬКОВІЙ ТЕХНІЦІ

The article deals with the formation of surfaces by the movement of auxiliary surfaces (intermediaries), based on a parametric kinematic screw. In modern existing mechanisms operating at high speeds, dynamic loads exceed static loads many times over. To improve accuracy, it is necessary that the process of processing a part with a cutting tool is carried out taking into account the coefficient of uneven rotation in the manufacture of products. The technology of processing complex surfaces by the most productive method of running is closely related to the profiling of conjugated curved surfaces formed by the movement of intermediaries. The mating curved surfaces of kinematic pairs, with different parameters, in turn, require the development of effective kinematic methods of formation and ensuring high accuracy in the processing of products used in mechanisms and processed on automatic lines, allowing to expand the scope of the most advanced methods of processing complex products.. Proposed  1 the kinematic screw has proven itself well for the formation of mating ruled surfaces, so the urgent task arose to investigate the possibility of using a kinematic screw for mating curved surfaces. The study was developed on the basis of the proven theorem of Academician A.N. Podkorytov, about instantaneous envelope axoids, which allows you to form mating curved surfaces, using a parametric kinematic screw, which takes into account the kinematics of metal-cutting machines. When solving the set task, the design time is reduced and the accuracy of manufacturing parts in the mechanisms of weapons and military equipment is increased. Keywords: kinematic method, geometric model, cutting tool, mating surfaces, curved surface, parametric kinematic screw, auxiliary surface (intermediaries), instantaneous axoid envelopes.

An Automatic Detection Framework for Electrical Anomalies in Electrified Rail Transit System

Automatic electrical anomaly detection for high-speed rail (HSR) is essential to ensure the safe and efficient operation of HSR. In this article, a novel anomaly detection method is proposed to rapidly identify typical anomalies and detect unknown anomalies in train-grid electrical coupling system (TGECS). The framework of the method consists of feature extraction, pre-classification and re-identification. First, the raw electrical signal is processed by Hilbert transform (HT) to obtain the instantaneous envelope. Second, the one-dimensional (1-D) convolutional neural network (CNN) is adopted to learn deeper features and perform pre-classification. To reduce the number of parameters, the global average pooling layer is used instead of the fully connected layers. To further improve the recognition accuracy, the additive angular margin named arcface is used to enhance the discriminative power of the model. Finally, the re-identification operation determines the final result by calculating the distances between the features. The accuracy of the proposed method for typical electrical anomaly recognition is beyond 99% and for unknown anomaly detection exceeds 86%. The experimental results demonstrate the effectiveness and the superior performance of the proposed method in terms of recognition accuracy, unknown anomalies detection and computational complexity, compared to the NN, 2-D CNN, LSTM and CNN-LSTM.

Torsion pendulum measurement method for time varying moment of inertia

A time-varying moment of inertia (MOI) measurement model based on the torsion pendulum method is studied. According to the principle of dynamics, the motion equation of torsion pendulum with single degree of freedom is established by using the second Lagrange equation modeling, which indicates the time-varying MOI can be obtained by the instantaneous undamped natural frequency of torsional pendulum motion and the torsion bar coefficient. Among them, the instantaneous frequency is calculated by the instantaneous angular frequency and envelope signal of angular displacement using Hilbert transform. The torsion bar coefficient is calibrated by the standard weight with known MOI, and has been compensated by temperature. Based on air floating turntable, the torsion pendulum system is built to measure the MOI of fine sand in the funnel, which can minimize the effects of mechanical friction. The relative error of MOI between calculating the result and measuring the value is less than 0.65%, which verifies the effectiveness of the proposed time varying temperature compensation method.

Modal Parameter Identification of Time-Varying and Weakly Nonlinear Systems Based on an Improved Empirical Envelope Method

The empirical envelope (EE) method based on the amplitude-modulation and frequency-modulation (AMFM) decomposition is effective for identifying the modal parameters of time-varying and weakly nonlinear systems. However, the identification accuracy of the EE method is sensitive to noises which often exist in vibration measurements of real structures. In this study, an improved empirical envelope (IEE) method is proposed to achieve robust modal parameter identification from noisy measurements. Specifically, the idea of sliding window threshold denoising is introduced to reduce the error in the instantaneous envelope induced by abnormal extreme points, and a moving average filter is utilized to reduce the error in the instantaneous frequency induced by high-frequency noises. Two numerical examples and an experimental example of a full-bridge aeroelastic model are analyzed to validate the accuracy of the IEE method and highlight the superiority of the IEE method relative to the original EE method. It is concluded that the IEE method is robust to measurement noises (the considered signal-to-noise ratios include 5–90[Formula: see text]dB) and that the IEE method is more accurate than the EE method. Hence, the IEE method serves as a promising alternative for modal parameter identification of time-varying and weakly nonlinear systems.

A High-Accuracy Tool Path Generation (HATPG) Method for 5-Axis Flank Milling of Ruled Surfaces with a Conical Cutter Based on Instantaneous Envelope Surface Modelling

In this paper, a novel framework of generating high-accuracy tool path for 5-axis flank milling of ruled surfaces with a conical cutter is developed by designing the instantaneous envelope surface of each planned cutter location (CL). Based on the analysis of the envelope theory, the instantaneous envelope profile of a selected cutter is only affected by the first-order derivation of the cutter’s motion, i.e. tangent vectors of the tool path. This property reveals an interesting fact that characteristic points on an envelope surface can be controlled via path tangents, thus laying the foundation for accurately evaluating the machining error at a single cutter location. On this basis, a simple yet robust method, called rotations for three-point offset (RTPO) method is first proposed to position a conical cutter to have at least three contact points with a ruled surface. Path tangents optimization is then performed to minimize the geometric deviation between the characteristic points and the ruled surface, followed by fine-tuning of the tool axis with a differential rigid motion. Both path tangent optimization and tool axis tuning are formulated as linear program problems, and the optimal cutter location with path tangents is determined by solving them repetitively. Finally, accurate tool path for conical cutter flank milling is generated with curve registration algorithms that interpolate the planned points and tangents. Examples are provided to validate the proposed method, and one can see high-accuracy flank milling paths can be planned point-by-point without subsequent optimization.

Classification of autism spectrum disorders individuals and controls using phase and envelope features from resting-state fMRI data

ABSTRACT This study uses phase and envelope features extracted from fMRI data for discriminating between Autism Spectrum Disorders (ASD) and control subjects. We exploit the transfer function perturbation (TFP) method to estimate the instantaneous phase and envelope of intrinsic resting-state brain network components from fMRI data. Then we calculated power, entropy, and coherency features. We examined three different classifiers and two different feature selection algorithms, in a way that the subsets of features that best predict classes were selected using a sequential forward feature selection (SFFS) algorithm and principal component analysis (PCA). Afterwards, three different categories of calculated features, including phase features, envelope features, and a combination of phase and envelope features, fed into non-linear Support Vector Machine (SVM), K-Nearest Neighbours (K-NN), and Deep Neural Network (DNN). Results illustrate that phase features are significantly discriminative and considerably improve ADSs and control subjects’ classification accuracy and lead to robust prediction. Moreover, 91% of classification accuracy was obtained when the dimension of phase features was reduced by PCA and fed to the non-linear SVM. Eventually, the two-sample t-test and Pearson’s correlation coefficient illustrated that phase features had a significantly lower correlation than envelope features.

GPR Data Interpretation Approaches in Archaeological Prospection

This article focuses on the possible drawbacks and pitfalls in the GPR data interpretation process commonly followed by most GPR practitioners in archaeological prospection. Standard processing techniques aim to remove some noise, enhance reflections of the subsurface. Next, one has to calculate the instantaneous envelope and produce C-scans which are 2D amplitude maps showing high reflectivity surfaces. These amplitude maps are mainly used for data interpretation and provide a good insight into the subsurface but cannot fully describe it. The main limitations are discussed while studies aiming to overcome them are reviewed. These studies involve integrated interpretation approaches using both B-scans and C-scans, attribute analysis, fusion approaches, and recent attempts to automatically interpret C-scans using Deep Learning (DL) algorithms. To contribute to the automatic interpretation of GPR data using DL, an application of Convolutional Neural Networks (CNNs) to classify GPR data is also presented and discussed.

On cycle-skipping and misfit function modification for full-wave inversion: Comparison of five recent approaches

Full-waveform inversion, a high-resolution seismic imaging method, is known to require sufficiently accurate initial models to converge toward meaningful estimations of the subsurface mechanical properties. This limitation is due to the nonconvexity of the least-squares distance with respect to the kinematic mismatch. We propose a comparison of five misfit functions promoted recently to mitigate this issue: adaptive waveform inversion, instantaneous envelope, normalized integration, and two methods based on optimal transport. We explain which principles these methods are based on and illustrate how they are designed to better handle kinematic mismatch than a least-squares misfit function. By doing so, we can exhibit specific limitations of these methods in canonical cases. We further assess the interest of these five approaches for application to field data based on a synthetic Marmousi case study. We illustrate how adaptive waveform inversion and the two methods based on optimal transport possess interesting properties, making them appealing strategies applicable to field data. Another outcome is the definition of generic tools to compare misfit functions for full-waveform inversion.

Modelling and experiment of gear hob tooth profile error for relief grinding

Gear hob is an important tool that is most used in gear processing. Hob accuracy directly exerts an overwhelming influence on the quality of the processed gear. Generally, the hob tooth profile accuracy is mainly determined by relief grinding process. Studies on tooth profile errors of gear hobs caused by severe friction and cutting with the high-speed rotation of the wheel during the form grinding machining of hobs are limited. Thus, a theoretical model of the tooth profile error prediction under different machining parameters was established based on the analysis of coupling influence of high temperature and high strain rate on gear hobs in the relief grinding process. The model was completed on the basis of the dynamic explicit integral finite element method of thermo-mechanical coupling. Through the prediction model, the influence of the grinding depth ap, feed speed Vw and grinding speed Vs on the tooth profile error can be analysed. In addition, an algorithm for accurately calculate the grinding wheel axial profile by combining instantaneous envelope theory and hob normal tooth profile was proposed. The hob relief grinding experiments were carried out using the proposed grinding wheel profile algorithm. The relative error of the prediction obtained by comparing the calculation results of the prediction model with the experimental results is within 10%. Results prove the validity of the prediction model. This finding is greatly important for optimising the accuracy of hob relief grinding.

Enhancement of adaptive mode decomposition via angular resampling for nonstationary signal analysis of rotating machinery: Principle and applications

Vibration signal analysis provides an effective approach for condition monitoring and fault diagnosis of rotating machines. Under time-varying conditions, vibration signals feature nonstationarity, consist of multiple frequency components, and usually have spectral overlaps. Adaptive mode decomposition methods can extract mono-components from a given multi-component signal to meet the requirement for estimating instantaneous frequency. Among them, the recently proposed methods, including empirical wavelet transform, variational mode decomposition and Fourier decomposition method, outperform the classic empirical mode decomposition in terms of rigorous mathematical formulation. Nevertheless, for multi-component signals with spectral overlaps, these three methods are subject to mode mixing and/or integrity issues, and fail to extract true mono-components, because they essentially separate mono-components based on spectral segmentation. In this paper, we propose a framework by exploiting the capability of angular resampling to address the mono-component overlapping issue. This methodology can separate true mono-components, thus facilitating accurate estimation of instantaneous frequency through Hilbert transform and generating perfect time–frequency representations (TFRs). Firstly, angular resampling is employed to make the constituent mono-components well separable in the frequency domain. Then, true mono-components are separated through adaptive mode decomposition. Next, informative mono-components are selected for further processing based on the prominent order information, and the selected mono-components are mapped into the time domain according to the relationship between the equal time and equal angle sampling. Finally, the instantaneous frequency and amplitude envelope of the recovered mono-components are calculated via Hilbert transform, and the TFR of raw signal is obtained by superposing the TFRs of all recovered mono-components. By doing so, the TFR achieves a fine time–frequency resolution and is free of both outer and inner interferences. The proposed methodology is demonstrated by simulated signal analysis, and further validated using the vibration data sets of three typical rotating machines (including a planetary gearbox in a wind turbine drivetrain, a civil aircraft engine and a hydraulic turbine rotor). The analysis results show its excellent capability to reveal the time-varying features of rotating machinery nonstationary signals.

Instantaneous frequency and its applications

The instantaneous frequency (IF) and envelope (IE) analysis of signals is very useful in various fields. In this paper, three examples of applications are presented. (1) Representing signals by IF and/or IE. Since signals before and after allpass filtering have the same frequency spectra, there is no way to distinguish them by the spectrum analysis. However, IF and/or IE may show clear differences between them. It is interesting that IF at the onset of a sinusoidal burst signal is twice the frequency of the sinusoid (JAES, September 1980). (2) Analyzing vibratos of violin sounds recorded in CDs in order to find out differences of playing styles of virtuosi such as Grumiaux, Oistrakh, Perlman, and Stern. Some results show clear differences in the speeds and depths of the frequency modulation (vibrato) (ISMA 2004). (3) Analyzing rotation fluctuations of automobile rotating shafts. An electromagnetic or electrostatic sensor located close to a rotating gear measures a quasi-sinusoidal signal. The IF analysis of this signal gives the rotation frequency and details of its fluctuation structure. Not only the steady state rotation, but transient rotation change analysis of a reciprocating engine at the sequence of idling stop and start (ISS) are also possible (https://www.aandd.co.jp/adhome/pdf/catalog/nvhanalysis/casra.pdf).

Seismic attribute analysis of Chicxulub impact crater

Chicxulub crater formed ~ 66 Ma ago by an asteroid impact on the Yucatan platform in the southern Gulf of Mexico. The crater has a ~ 200 km rim diameter and has been covered by carbonate sediments up to ~ 1.1 km thick in the central zone. Previous studies have identified the structure and major crater units through geophysical models from seismic reflection and potential field data, classified as the central uplift, terrace zone, outer and inner ring fault zones and impactite deposits. Impact produced a deep excavation cavity, with fragmentation and ejection of large volumes of crustal target rocks. Understanding the pre-existing structures, impact-induced deformation and post-impact processes requires high-resolution images of the crater and target zone. For this study, we use complex trace attributes of instantaneous phase, frequency, envelope amplitude and similarity, in an E-W seismic reflection profile crossing the crater in the marine sector. Geophysical logs and borehole lithological columns from the on-land drilling projects are used to constrain the petrophysical analysis. Seismic attributes aid to characterize the radial fault zones and physical property contrasts, revealing asymmetries in the crater structure. The reflector packages in the post-impact sediments and target Cretaceous sequence are identified in the frequency and phase attributes. The bottom crater reflectors, with the basal sediments filling the crater floor topography, are enhanced with the envelope amplitude attribute. A set of high-amplitude reflectors is shown in the similarity attribute, in which the reflector geometry is delineated on the target carbonate sequence. The offsets in the high-amplitude reflectors between the eastern and western sectors are possibly associated to target pre-impact asymmetries, impact deformation and effects of central crater collapse.

Broken rotor bar fault diagnostic for DTC Fed induction motor using stator instantaneous complex apparent power envelope signature analysis

The broken rotor bar is an unexpected fault and a common cause of induction motor failures that threaten the structural integrity of electric machines. In this paper, a new approach to a broken rotor bar diagnosis, without slip estimation, based on the envelope of the stator instantaneous complex apparent power (SICAP) is proposed. The envelope is obtained from the SICAP modulation and then transferred to a computer for monitoring the characteristic frequency and its amplitude using the Fast Fourier Transform (FFT). For this purpose, the winding function approach (WFA) is used to simulate the broken rotor bar occurrence in a squirrel cage induction motor (SCIM) fed on direct torque control (DTC). The obtained simulation results confirm the interest and efficiency of the proposed technique. Even when the induction motor is operating at the no-load level condition, the proposed method is also efficient to detect the broken rotor bar fault at low slip.

A Novel Algorithm for HRV Estimation from Short-Term Acoustic Recordings at Neck.

Heart rate variability (HRV) is an important noninvasive parameter to monitor the activity of the autonomic nervous system. This paper proposes an algorithm to analyze HRV by processing the acoustic data, recorded by placing a small, wearable sensor on the suprasternal notch (at neck) of an adult subject, primarily intended to record breathing sounds. The method used an empirical data analysis approach of the Hilbert-Huang transform (HHT) to construct an instantaneous energy envelope and segment the cardiac cycle by detecting S1 and S2 sounds using the K-means algorithm. The time-domain HRV analysis for the short-term recordings of 10 subjects demonstrated a close agreement with the reference ECG signal. The instantaneous heart rate (IHR) comparisons yielded an accuracy of 95.78% and 92.35% for S1 and S2 sounds respectively. The experimental results showed that the proposed algorithm can provide an accurate HRV analysis for the cardiac signals recorded at the neck.

Effect of traveling wave on the vortex-induced vibration of a long flexible pipe

It is expected that the vortex-induced vibration response of long flexible tubular structures exposed to a shear current will consist of multiple modes of standing and traveling waves simultaneously. The vibration characteristics of traveling wave caused by VIV and its effect on the vibration form for a flexible pipe model are presented in this paper. Two methods are proposed to reveal the characteristics of standing and traveling waves from the perspective of an instantaneous illustration and a time-averaged statistical analysis separately. The first is associated with the adoption of an illustration method depending upon an instantaneous local upper envelope to identify the dominance of the standing or traveling wave. It is found that the in-line and cross-flow wave types show synchronization in that the wave types are mostly changeless or occasionally transform from one type to the other nearly simultaneously. In both the in-line and cross-flow directions, it is evident that the vibration response is not dominated by the standing wave characteristics but can often be characterized by the alternate presence of a traveling wave and a hybrid standing-traveling wave. The second method is associated with the adoption of the significant strain peak concept proposed to estimate the occurrence of the peak strain response and fatigue damage risk. It is found that the strain peak value attributed to in-line static strain accounts for a large proportion of the final equivalent significant strain peak near the pipe end with high reduced velocities, especially when the flow velocity is high. With the occurrence of a hybrid pattern of standing-traveling wave for higher velocities, the distribution of the significant cross-flow strain peak becomes uniform and its corresponding wave shape is apt to be flat.