Time Frequency Domain Analysis Research Articles

A Study on the Time-Frequency Characteristics of Electromagnetic Radiation Signals during Concrete Cracking

At the moment of rupture, brittle materials such as concrete, coal, and rocks experience a sudden stress change that generates oscillating positive and negative charges on the crack surfaces, resulting in the emission of electromagnetic radiation (EMR) signals. While extensive investigations have focused on the EMR signals produced during the fracturing of coal and rocks, limited attention has been given to the signals arising from concrete cracking. Previous studies have predominantly concentrated on amplitude or frequency domain analysis, lacking a comprehensive exploration of the time-frequency domain characteristics of electromagnetic signals. In this study, various magnetic field sensors capable of collecting frequencies ranging from 50Hz to 50kHz are developed. A laboratory testing platform for concrete cracking is established with the utilization of data acquisition equipment and a rock point load tester. The EMR signals are subjected to time-frequency domain analysis using Short-Time Fourier Transform (STFT) methods. The study explores the relationship between the time-frequency domain characteristics of EMR signals and concrete dimensions, as well as the instantaneous stress drop at the moment of fracture. Experimental findings reveal that the dominant frequency of EMR signals generated during concrete cracking is in the low-frequency range. As the instantaneous stress drop at the moment of fracture increases, the frequency of the EMR signals shifts towards higher frequencies and the signals around 10kHz has a longer duration. When the frequency of EMR signals is lower than 20kHz, larger concrete dimensions result in greater stress drops at the moment of fracture, leading to higher intensity EMR signals. Utilizing the EMR characteristics of concrete cracking holds promise for monitoring the safety and stability of concrete structures.

Indian energy policies amidst urban population and economic growth: Sourcing evidence from time-frequency domain analysis

Relying solely on fossil fuel-driven economic growth could prove unsustainable, particularly in India, where the adverse environmental impact of this growth trajectory is increasingly affecting overall economic development. This challenge may hinder the realization of the sustainable development goals (SDGs) in the country. Therefore, to address this policy concern, an empirically investigation on the influence of various growth drivers on India's economic growth from 1965 to 2019 was conducted by this present study. Leveraging time-frequency estimators like gradual shift causality tests and Wavelet Coherence, the study yields a range of findings that can inform the formulation of an SDG-oriented policy framework. These outcomes not only acknowledge the detrimental environmental consequences of economic growth but also provide insights into addressing energy security issues. Ultimately, this research enhances to the literature by suggesting a comprehensive and adaptable policy framework aimed at advancing SDGs 7, 8, 12, and 13.

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task.

The S-transform is a fundamental time-frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST). Initially, it resolves the compatibility issue between the S-transform and the synchrosqueezing strategy through phase-shifting. Subsequently, the SS-GPST accomplishes spectral energy focusing and resolution enhancement via a generalized parameter and synchrosqueezing. A synthetic signal test shows that the SS-GPST excels over other methods at focusing degree, spectral resolution, and signal reconstruction accuracy and speed. In actual GPR tunnel detection data processing, we assess the adaptability of the SS-GPST from three aspects: spectral energy distribution, thin layer identification, and data denoising. The results indicate: (1) compared to other methods, the SS-GPST accurately expresses spectral components with a strong focusing degree and fewer interference components; (2) high-frequency slices of the SS-GPST accurately detect the top and bottom interfaces of a 3.0-3.5 cm reinforcement protection layer; and (3) due to fewer interference components in the SS-GPST spectrum, reconstructing GPR profiles through the SS-GPST inverse transform is an efficient denoising technique. The SS-GPST demonstrates adaptability to different data processing purposes, offers high-resolution T-F spectra, and shows potential to supersede the S-transform.

A novel method for measuring roll angle

The precise measurement of the spin speed of a high-speed autonomous unmanned aerial vehicle (HSA-UAV) is a key element in mastering flight stability, and the measurement of roll angle is the key to determining the accuracy of navigation control systems. We put forward a novel method for measuring roll angle. This method starts from the time–frequency domain analysis of the output signal of the geomagnetic sensor, extracts the time–frequency ridge of the time–frequency matrix (TFM) to obtain the spin speed of the HSA-UAV, and reconstructs the output signal of the geomagnetic sensor. It can calculate the roll angle when the calibration parameters of the geomagnetic sensor are unknown and have good engineering practical value. In addition, we also propose an improved nonlinear short-time Fourier transform with high-frequency resolution and a forward penalty dynamic path ridge-extraction method with frequency jump suppression to extract instantaneous frequency in the TFM.

Trend change analysis in the assessment of body balance during posture adjustment in reaction to anterior-posterior ground perturbation.

Postural adjustments (PA) occur to counteract predictable perturbations and can be impaired as a result of musculoskeletal and neurological dysfunctions. The most common way to detect PA is through electromyography measurements or center of pressure (COP) position measurements, where analysis in time domain and frequency domain are the most common. Aim of the research was to determine whether a new method of analyzing stabilographic measurements-the COP trend change analysis (TCI) of temporary posture corrections- can expand understanding of changes in balance strategy connected with PA. The study group involved 38 individuals (27women, 11men) aged 23±2.6 years. Measurements were performed using a stabilographic platform placed on a perturbation platform. The tests involved three measurements with forward and backward momentary movements of the platform. Participants were tested in three conditions-knowing the nature, time and direction of perturbation (Tr3), knowing only the nature of perturbation (Tr2) and without any information about the perturbation (Tr1). Statistically significant differences were revealed in the last second of Tr3 for the mean velocity of COP (p<0.05) and for two TCI parameters-TCI_dV (p<0.05) and TCI_dS (p<0.01). The increase in TCI_dV was related to the increase in the mean distance between trend changes (TCI_dS) and constant value of the mean time between trend changes (TCI_dT). The increase of the mean value of TCI_dS was the result of smaller number of posture corrections with the distance 0-2 mm and lager number with the distance 4-6 mm. Obtained results proved that the TCI analysis is a method enabling an extended analysis of PA, indicating the nature of changes occurring in posture corrections-longer momentary jumps of COP-related to a change in the strategy of maintaining balance before a known disorder, which has not been analyzed in this type of research so far.

Seismic data AVO analysis in time frequency domain using synchroextracting transform

Time-frequency analysis is a crucial tool for processing and interpreting seismic data in hydrocarbon exploration. Accurately determining the temporal and spectral characteristics is essential for applications such as reservoir characterization and fluid identification. High-resolution representation of seismic data time-frequency properties improves reliability in these applications. Recent advancements in post-processing techniques aim to enhance conventional methods. The synchroextracting transform (SET) is an innovative approach that combines a post-processing procedure with the short-time Fourier transform. It retains time-frequency information associated with spectral components while eliminating excessive energy dispersed in the time-frequency domain. This study evaluates the effectiveness of SET for seismic data time-frequency analysis, comparing it with contemporary post-processing techniques using synthetic signals and field seismic data. Results show that SET provides higher resolution for distinct signal components and effectively reduces energy dispersion. SET exhibits advantages such as robustness against random noise, reasonable computational cost, and offering more interpretable time-frequency information regarding attenuated amplitudes. Building on synthetic experiments, we demonstrate the capability of SET to generate high-resolution single frequency sections of seismic data, facilitating accurate analysis of amplitude variation with offset (AVO) in the time-frequency domain.

Cross-influence of cavitation and flow rate on pressure pulsation of a volute mixed flow pump

Mixed-flow pump is a general purpose hydraulic machinery in many fields of fluid transport for its advantages of wide efficient operation flow rate range, but its operation stability is restricted by cavitation. To obtain the cross effect of cavitation and flow rate on its pressure pulsation, a high-precision experimental system was first established to monitor the pressure signals at four key positions, obtaining their time domain; second, based on this tested results, time frequency domain analysis technique based on continuous wavelet transform was adopted to capture the temporal evolution; third, wavelet coherence value analysis was further adopted to diagnose the cavitation development speed at different spatial positions. Primary findings are as follows: (1) the secondary peaks induced by cavitation generated the discrete disturbance in low-frequency range, and the amplitudes at shaft frequency and blade passing frequency were both increased, with a worse time continuity. (2) Under 1.0 QBEP, the mixed flow pump had a best anti-cavitation performance. Under 0.8 QBEP, its internal flow pattern was easily to be disturbed by cavitation flow, while that under 1.2 QBEP had the fastest cavitation development speed. (3) Under the action of unstable potential flow, within the flow field near the rotor–static interface and the downstream, a close flow exchange was established between the cavitation bubbles and mainstream; thus, the pressure pulsation inside the volute became more sensitive to the cavitation development.

Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression.

Surface roughness prediction is a pivotal aspect of the manufacturing industry, as it directly influences product quality and process optimization. This study introduces a predictive model for surface roughness in the turning of complex-structured workpieces utilizing Gaussian Process Regression (GPR) informed by vibration signals. The model captures parameters from both the time and frequency domains of the turning tool, encompassing the mean, median, standard deviation (STD), and root mean square (RMS) values. The signal is from the time to frequency domain and it is executed using Welch's method complemented by time-frequency domain analysis employing three levels of Daubechies Wavelet Packet Transform (WPT). The selected features are then utilized as inputs for the GPR model to forecast surface roughness. Empirical evidence indicates that the GPR model can accurately predict the surface roughness of turned complex-structured workpieces. This predictive strategy has the potential to improve product quality, streamline manufacturing processes, and minimize waste within the industry.

Mechanical Identification Method of Amplitude Warning False Alarm Points Based on Dynamical Time–Frequency Domain Analysis

Mechanical Identification Method of Amplitude Warning False Alarm Points Based on Dynamical Time–Frequency Domain Analysis

When "more for others, less for self" leads to co-benefits: A tri-MRI dyad-hyperscanning study.

Unselfishness is admired, especially when collaborations between groups of various scales are urgently needed. However, its neural mechanisms remain elusive. In a tri-MRI dyad-hyperscanning experiment involving 26 groups, each containing 4 participants as two rotating pairs in a coordination game, we sought to achieve reciprocity, or "winning in turn by the two interacting players," as the precursor to unselfishness. Due to its critical role in social processing, the right temporal-parietal junction (rTPJ) was the seed for both time domain (connectivity) and frequency domain (i.e., coherence) analyses. For the former, negative connectivity between the rTPJ and the mentalizing network areas (e.g., the right inferior parietal lobule, rIPL) was identified, and such connectivity was further negatively correlated with the individual's final gain, supporting our task design that "rewarded" the reciprocal participants. For the latter, cerebral coherences of the rTPJs emerged between the interacting pairs (i.e., within-group interacting pairs), and the coupling between the rTPJ and the right superior temporal gyrus (rSTG) between the players who were not interacting with each other (i.e., within-group noninteracting pairs). These coherences reinforce the hypotheses that the rTPJ-rTPJ coupling tracks the collaboration processes and the rTPJ-rSTG coupling for the emergence of decontextualized shared meaning. Our results underpin two social roles (inferring others' behavior and interpreting social outcomes) subserved by the rTPJ-related network and highlight its interaction with other-self/other-concerning brain areas in reaching co-benefits among unselfish players.

Train–Bridge Coupled Vibration of a Long-Span Steel Truss Suspension Bridge Under Complex Driving Conditions

To investigate the vibration characteristics of long-span suspension bridges under the impact of high-speed trains, a method for conducting a time–frequency domain analysis of the dynamic response of a coupled system is presented in this study. First, the finite element model of a suspension bridge is established to conduct modal analysis, and simulation results are compared with measured data to validate the correctness of the bridge model. Subsequently, a multi-body dynamics model of a high-speed train is developed. Then, two approaches, the dummy method and flexible-track method, are employed to create the train–bridge coupled system. Finally, the flexible-track method is used to perform time–frequency domain analysis of the system under complex operational conditions. The research revealed that large-span suspension bridges exhibit lower natural frequencies. The vertical bending vibrations of the main beam have the potential to significantly impact train operations. Under the combined impact of cable tension and wheel–rail forces, the vibration patterns of large-span suspension bridges become notably intricate. The number of trains and their loading configurations both exert influence over the dominant frequencies and amplitudes in the PSD of bridges. In the complex traffic of large-span suspension bridges, the rational arrangement of travel lanes can reduce the possibility of resonance in the bridge–train system.

Quantifying the Entropy and Enthalpy of Insertion Materials for Battery Applications Via the Multi-Species, Multi-Reaction Model

The entropy coefficient of a battery cell is the property that governs the amount of reversible heat that is generated during operation. In this work, we propose an extension of the Multi-Species, Multi-Reaction (MSMR) model to capture the entropy coefficient of a large format lithium-ion battery cell. We utilize the hybridized time-frequency domain analysis (HTFDA) method using a multi-functional calorimeter to probe the entropy coefficient of a large format pouch type lithium-ion battery with a NMC 811 cathode and a graphite anode. The measured entropy coefficient profile of the battery cell is deconvoluted into an entropy coefficient for each active material, which is then estimated using an extension of the MSMR model. Finally, we extend the entropy of a material to individual entropy for each gallery as treated by the model.

Spectrogram correlated stacking: A novel time-frequency domain analysis of the stochastic gravitational wave background

Spectrogram correlated stacking: A novel time-frequency domain analysis of the stochastic gravitational wave background

Experimental Study on the Identification and Diagnosis of Dynamic Crack Propagation in the Piston Rods of Process Gas Compressors in Underground Gas Storage

Ensuring the reliability of process gas compressors is critical for underground gas storage, as piston rod fractures can lead to serious accidents, such as natural gas leaks or explosions. On-time monitoring and early detection play a vital role in preventing catastrophic consequences, minimising costs, and reducing production losses due to unplanned downtime. This study presents a novel accelerated life-testing method designed to replicate the fracture events of reciprocating compressor piston rods. By accelerating the induced crack initiation and propagation to the final fracture, comprehensive analyses of the fracture results are performed to reveal the piston rod fracture mechanism and the resulting secondary damage to the unit. The research further presents an innovative approach for identifying piston rod crack propagation by means of acoustic emission. Through kinetic analysis and time–frequency domain analysis, the study elucidates two mechanisms responsible for triggering crack signals during the compressor operation: the contact impact between the crosshead pin and the bearing due to the piston rod load reversal, and crack propagation occurring before the maximum tensile load is reached. In addition, the study identifies the piston rod crack expansion signal frequency band and achieves a high-sensitivity identification of crack dynamic growth by extracting signal sub-band features associated with crack propagation. Then, a prediction model of the fatigue crack growth rate was established based on the AE energy release rate, which provides a quantitative assessment of dynamic crack propagation during compression. This method aims to provide a maintenance strategy for piston rod fractures, thereby increasing the operational safety of critical dynamic equipment in underground gas storage.

Polarization Analysis in Time–Frequency Domain by Complex Spectral Matrix: Application to Various Phases of Seismogram

Polarization Analysis in Time–Frequency Domain by Complex Spectral Matrix: Application to Various Phases of Seismogram

Multi-Resolution Expansion of Analysis in Time-Frequency Domain for Time Series Forecasting

Multi-Resolution Expansion of Analysis in Time-Frequency Domain for Time Series Forecasting

The Value and Use of Chinese Traditional Music in Dance Performance in the Internet Era

Abstract In this study, Gaussian mixture model preprocessing and histogram of orientation gradient (HOG) and histogram of optical flow direction (HOF) action feature extraction techniques are used to significantly optimize the accuracy of dance performance action recognition. In addition, the VNect pose estimation algorithm and dual-stream 3D-CNN-based skeleton action recognition are used to improve significantly the accuracy and efficiency of recognition. In terms of music feature recognition, this study utilizes short-time time domain and frequency domain analysis techniques and BP neural networks to effectively achieve accurate recognition and sentiment classification of music features. Analyzing a dataset containing 6000 training videos, 800 validation videos, and 1200 test videos, the study reveals that musical elements, melody, rhythm, emotional tone, and song style significantly impact the stage performance effect. The study not only highlights the importance of traditional Chinese music in dance performance to enhance the artistic effect and deepen the emotional expression, but also opens up new perspectives and methods for disseminating and developing traditional culture.

Diagnosis of localized defects in floating bush bearings through time-frequency domain analysis

Bearings play a crucial role in the functionality of rotating machinery, and any defects in these components can result in machine failure. Detecting, diagnosing, and prognosing bearing faults are crucial steps in machine failure diagnostics to prevent malfunctions and breakdowns. While various methods exist for fault detection, including acoustic emission analysis, visual inspection, thermography, ultrasonic, motor current analysis, wear-debris analysis, oil analysis, and vibration analysis, the latter stands out as a popular non-destructive method. This paper focuses on time and frequency domain vibration analysis techniques for detecting faults in floating bush bearings. Particularly beneficial for online monitoring, remote and non-human intervention areas, and hazardous locations, the time-frequency domain approach enhances diagnostic capabilities. The vibration data collected during these experiments has been rigorously analyzed using data acquisition system and applied a comprehensive approach that includes evaluating the data in both the time and frequency domains, as well as utilizing advanced signal processing techniques, notably the high-frequency resonance technique. Test results underscore the effectiveness of specific parameters in identifying defects: waveform, form factor, parameter K, and cepstrum excel in pinpointing external defects, while kurtosis, crest factor, and skewness prove adept at identifying internal faults. In the frequency domain, the enveloped spectrum emerges as a robust method for comprehensive defect detection. The vibration data presented in this paper will prove to be an invaluable resource for professionals engaged in the field of vibration measurement and analysis.

CFI-LFENet: Infusing cross-domain fusion image and lightweight feature enhanced network for fault diagnosis

Data-driven fault diagnosis has become a hot topic of research in recent years, due to its wide applicability, high accuracy, and ease of modeling. In data-driven fault diagnosis, feature extraction is usually performed through a combination of one-dimensional time domain or frequency domain analysis methods. However, the constraints of data format and diagnostic methods give rise to three primary issues: inadequate utilization, expression, and mining of fault features, thereby impeding the broader application of data-driven methods. This paper presents a fault diagnosis framework that integrates cross-domain fusion images and a lightweight feature-enhanced network to address these issues. We leverage the temporal and frequency features of raw time series data, transforming them into images. This approach not only offers a greater amount of fault information but also highlights the inherent features more prominently. In terms of diagnosis methods, a novel feature-enhanced network model is proposed. This model takes images generated by a cross-domain image fusion module as inputs and then goes through a customized feature extraction module, a feature fusion module, a feature enhancement module, and a classification module to achieve high-precision and fast fault diagnosis. The effectiveness of this framework is validated on two publicly available datasets. The results show that the average accuracy of this fault diagnosis framework can reach 100%, surpassing the findings of existing research. The experiments on noise resistance further showcase the superiority of the proposed framework. It is expected that the framework presented in this study will provide a new perspective and direction for fault diagnosis.

Mixed-flow pump cavitation characteristics extraction based on power spectrum density through pressure pulsation signal analysis

Mixed-flow pump is an important machine for transporting fluids, which has the advantages of both centrifugal pump and axial flow pump, but it is prone to cavitation, endangering efficiency and safe operation. A 20 m × 8.5 m high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were built for the study of cavitation signals. The detection of pressure pulsation signal is a direct measurement of the cavitation region, and the decay of information is less. In this study, the time domain and frequency domain characteristics of water pressure pulsation signal are analyzed by using fast Fourier transform method. The power spectrum density (PSD) is used to extract the characteristics of each cavitation stage, especially the full cavitation stage with random signal characteristics. The results show that PSD method reflects more information and is more sensitive than traditional time–frequency domain analysis. The characteristic power spectrum (PS) value of the cavitation of the mixed-flow pump machinery can be given through the feature extraction of the PS, which plays a great role in the effective identification and prevention of cavitation.