Joint Time-frequency Analysis Technique Research Articles

Synthesis of Linear JTFA-Based Response Spectra for Structural Response and Seismic Reduction Measures for North-East India

North-East India (NEI) has a long history of devastating earthquakes due to the complicated tectonic setting of the region. A shortage of sufficient recorded time-histories from the region calls for a synthesis of accelerograms for dynamic analyses. In this study, a novel Joint Time-Frequency Analysis (JTFA) technique is adopted for the synthesis of accelerograms, considering the non-stationary behavior of earthquake waves. JTFA is used for analyzing the signals in a joint time and frequency domain to better understand its characteristics and synthesize signals without compromising its inherent characteristics like frequency content and amplitude. Synthetic accelerograms are developed using JTFA techniques for different magnitude and distance ranges between 5 to 6.8 and 0–480[Formula: see text]km and response spectra are developed. Synthesized generalized accelerograms and their response spectra are compared with actual signals in the same magnitude-distance ranges and were found to match. A comparison of the frequency contents of actual and synthetic signals was also carried out using Fourier Transforms and spectrograms (SPs) and was found to be in good agreement. Further, a comparative study of various earthquake reduction measures for NEI is carried out for a scenario earthquake using the synthesized data, and the best suitable structural input for the region is recommended.

Joint time-frequency analysis of dynamic cerebral autoregulation using generalized harmonic wavelets

Objective: To develop a joint time-frequency analysis technique based on generalized harmonic wavelets (GHWs) for dynamic cerebral autoregulation (DCA) performance quantification. Approach: We considered two groups of human subjects to develop and validate the method: 55 healthy volunteers and 35 stroke-free subjects with unilateral internal carotid artery stenosis (CAS). We determined the mean and coherence-weighted average of the phase shift (PS) of appropriately defined GHW-based transfer functions, based on data points over the joint time-frequency domain. We compared agreement of standard transfer function analysis (TFA) and GHW analyses in healthy subjects using Bland-Altman plots. We assessed sensitivity of each metric to detect the presumed side-to-side difference in DCA function in CAS subjects (with decreased PS on the occluded side), using McNemar’s chi square test to compare each metric to the standard TFA approach. An alternative Morlet wavelet-based approach was also considered. Main results: The GHW and TFA methods exhibited strong agreement in healthy subjects. Among CAS subjects, GHW metrics outperformed TFA and Morlet wavelet-based approaches in identifying expected side-to-side differences: TFA sensitivity was 40.0% (95%CI 23.9–57.9), Morlet 60.0% (95%CI 42.1–76.1), and GHW >70% for both metrics (GHW mean PS sensitivity 74.3, 95%CI 56.7–87.5, p = 0.0027 versus TFA; GHW coherence-weighted PS sensitivity 71.4, 95%CI 53.7–85.4, p = 0.0009 versus TFA). Significance: In comparison to the widely used stationary Fourier transform-based TFA and to Morlet wavelet-based analysis, our data suggest that the GHW-based analysis performs better in identifying DCA asymmetry between the two cerebral hemispheres in patients with high grade unilateral carotid stenosis. Our method may provide enhanced confidence in employing DCA metrics as a sensitive diagnostic tool for detecting impaired DCA function in a variety of pathological settings.

On the radiated EMI current extraction of dc transmission line based on corona current statistical measurements

Corona-originated discharge of DC transmission lines is the main reason for the radiated electromagnetic interference (EMI) field in the vicinity of transmission lines. A joint time-frequency analysis technique was proposed to extract the radiated EMI current (excitation current) of DC corona based on corona current statistical measurements. A reduced-scale experimental platform was setup to measure the statistical distributions of current waveform parameters of aluminum conductor steel reinforced. Based on the measured results, the peak value, root-mean-square value and average value with 9 kHz and 200 Hz band-with of 0.5 MHz radiated EMI current were calculated by the technique proposed and validated with conventional excitation function method. Radio interference (RI) was calculated based on the radiated EMI current and a wire-to-plate platform was built for the validity of the RI computation results. The reason for the certain deviation between the computations and measurements was detailed analyzed.

An artificial neural network approach for stochastic process power spectrum estimation subject to missing data

An artificial neural network (ANN) based approach is developed for estimating the power spectrum of stochastic processes subject to missing/limited data. In this regard, an appropriately defined ANN is utilized to capture the stochastic pattern in the available data in an average sense. Next, the extrapolation capabilities of the ANN are exploited for generating realizations of the underlying stochastic process. Finally, power spectrum estimates are derived based on established frequency (e.g. Fourier analysis), or versatile joint time–frequency analysis techniques (e.g. wavelets) for the cases of stationary and non-stationary stochastic processes, respectively. One of the significant advantages of the approach relates to the fact that no a priori knowledge about the data is assumed, while the approach is applicable for treating non-stationary processes not only with separable but non-separable in time and frequency evolutionary power spectra as well. Comparisons of several target power spectra with Monte Carlo simulation based power spectrum estimates demonstrate the versatility and reliability of the approach for up to 50% missing data.

Direct measurement of group delay with joint time-frequency analysis of a white-light spectral interferogram

We propose and demonstrate an analysis method to directly extract the group delay rather than the phase from the white-light spectral interferogram. By the joint time-frequency analysis technique, group delay is directly read from the ridge of wavelet transform, and group-delay dispersion is easily obtained by additional differentiation. The technique shows reasonable potential for the characterization of ultra-broadband chirped mirrors.

Nondestructive Evaluation in Materials Using Time-Frequency Analysis Methods

Application of signal processing techniques to nondestructive evaluation (NDE) in general has become a standard tool in determining the frequency characteristics of the signals and relating these characteristics to the integrity of the structure under consideration. The joint time-frequency analysis techniques are applied to analyze ultrasonic signals in degraded austenite stainless 316 steel, to study the evaluation of damage in this material. It is demonstrated that the nonstationary characteristics of ultrasonic signals could be analyzed effectively by these methods. WVD was found to be more effective for analyzing the attenuation and frequency characteristics of degraded materials through ultrasonic. It is indicated that the time-frequency analysis methods should also be useful in evaluating various damages and defects in structural members.

Detection of a manoeuvring air target in sea-clutter using joint time–frequency analysis techniques

Traditionally, radar signals have been analysed in either the time or the frequency domain. Joint time–frequency representations characterise signals over a time–frequency plane. They thus combine time-domain and frequency-domain analyses to yield a potentially more revealing picture of the temporal localisation of a signal's spectral components. Therefore, for air target returns with time-varying frequency content, joint time–frequency representations offer a powerful analysis tool. A concise review of time–frequency transforms is provided as background and is needed to appreciate how time–frequency processing methods can improve conventional time or frequency processing methods. The authors use time–frequency analysis techniques for the detection of a manoeuvring aircraft using high frequency (HF) radar in heavily cluttered regions. They compare the ability of different time–frequency transforms to resolve several experimental aircraft returns. The relative speeds of the different transforms are also quantitatively studied. The results clearly demonstrate that time–frequency analysis techniques can significantly improve the detection performance of the HF radar and add considerable physical insight over what can be achieved by conventional Fourier transform methods currently used by HF radars.

Characterization of the Korotkoff sounds using joint time–frequency analysis

The sounds associated with the five classical Korotkoff phases are clinically important for measuring systolic and diastolic blood pressures. The frequency ranges of the sounds have already been described simply using the overall peak frequencies within each phase by Fourier methods. However, such analysis may be missing potentially useful clinical information. The aim of this study was to compare features associated with the different phases of the Korotkoff sounds obtained during blood pressure measurement using a joint time–frequency analysis (JTFA) technique. A single operator recorded Korotkoff sounds from 25 healthy subjects using a measurement system comprising cardiology stethoscope, microphone, amplifier and recording system for computer sound digitization, and a MiniDisc system for playback to the cardiologist for Korotkoff phase classification. We have shown that using this system the phase classification by the cardiologist is repeatable, with no significant differences found in the number of sounds allocated to phases on two separate recording assessments. The digitized sounds were processed using a MATLAB-based short-time Fourier transform JTFA technique and differences in time, frequency and amplitude characteristics between the phases compared. It was found that on average, phase III had the largest overall amplitude and high frequency energy. Phase II had the greatest high frequency component and longest murmur, and was visibly the most complex phase in terms of time and frequency content. In contrast, phases IV and V had the lowest amplitude and frequency components. Overall, the statistically significant transitions between phases were: phase I to II with increases in high frequency (224 to 275 Hz) (p < 0.01) and sound duration (49 to 98 ms) (p < 0.0001), II to III with a significant decrease in sound duration (to 37 ms) (p < 0.0001), III to IV with decreases in maximum amplitude (0.95 to 0.25), highest frequency (262 to 95 Hz), and relative high frequency energy of the sounds (0.61 to 0.10) (all p < 0.0001), and IV to V with decreases in the maximum amplitude (0.25 to 0.13) (p < 0.0002) and high frequency energy (0.10 to 0.03) (p < 0.005). This study has demonstrated that joint time–frequency analysis of Korotkoff sounds was able to identify characteristic differences associated with the different phases classified by the expert cardiologist. Ultimately, exploiting the joint time and frequency characteristics of the sounds may improve blood pressure measurement and help to assess the stiffness of the peripheral arteries.

Quantitative SNR analysis for ISAR imaging using joint time-frequency analysis-Short time Fourier transform

V.C. Chen recently presented an inverse synthetic aperture radar (ISAR) imaging technique using the joint time-frequency analysis (JTFA), which has been shown having a better performance for maneuvering targets over the conventional Fourier transform method. The main reason is because the frequencies of the radar returns of the maneuvering targets are time varying and a JTFA is a technique that is suitable for such signals, in particular a JTFA may concentrate a wideband signal, such as a chirp, while it spreads noise. We quantitatively study the signal-to-noise ratio (SNR) in the ISAR imaging using one of the typical JTFA techniques, namely the short time Fourier transform (STFT). We show that the SNR increases in the joint time-frequency (TF) domain over the one in the time or the frequency domain alone both theoretically and numerically. This quantitatively shows the advantage of the JTFA technique for the ISAR imaging.

Simulations of non‐stationary frequency content and its importance to seismic assessment of structures

AbstractTo verify the importance of the non‐stationary frequency characteristic of seismic ground motion, a joint time–frequency analysis technique of time signals, called chirplet‐based signal approximation, is developed to extract the non‐stationary frequency information from the recorded data. The chirplet‐based signal approximation is clear in concept, similar to Fourier Transform in mathematical expressions but with different base functions. Case studies show that the chirplet‐based signal approximation can represent the joint time–frequency variation of seismic ground motion quite well. Both the random models of uniform modulating process and evolutionary process are employed to generate artificial seismic waves. The joint time–frequency modulating function in the random model of evolutionary process is determined by chirplet‐based signal approximation. Finally, non‐linear response analysis of a SODF system and a frame structure is performed based on the generated artificial seismic waves. The results show that the non‐stationary frequency characteristic of seismic ground motion can significantly change the non‐linear response characteristics of structures, particularly when a structure goes into collapse phase under seismic action. It is concluded that non‐stationary frequency characteristic of seismic ground motion should be considered for the assessment of seismic capacity of structures. Copyright © 2002 John Wiley &amp; Sons, Ltd.

伝熱管伝播音に基づく流動層ボイラの流動状態監視

The newly developed monitor watches the fluidized condition of FBC (Fluidized Bed Combustor), referring to acoustic signals which are collected by heating tubes in the beds and passing to tube acceleration sensors outside the fluidized Beds. The monitor, without any observation hole or special probes, not only detects the bad-fluidized region, which requires proper operations for preventing from agglomeration, but also diagnoses conditions of the boiler equipment. The monitor adopts the joint time-frequency analysis technique to the acoustic signals as well as considers non-Gaussian properties, on decision making procedure, including skewness and excess of parameter distributions which are the resulting characteristics through the analyses on the time-frequency plain. The monitor has been applied to an 350MWe FBC for testing, and validated through one and a half year operation

WAVELET ANALYSIS OF VIBRATION SIGNALS OF AN OVERHANG ROTOR WITH A PROPAGATING TRANSVERSE CRACK

This paper presents an experimental study of the dynamic response of an overhang rotor with a propagating transverse crack using the discrete wavelet transform (DWT)—a joint time frequency analysis technique. Start-up and steady state vibration signatures are analyzed using Daubechies (Db6) mother wavelet and the results are presented in the form of scalograms and space-scale energy distribution graphs. The start-up results showed that crack reduces the critical speed of the rotor system. The steady state results showed that propagating crack produces changes in vibration amplitudes of frequency scale levels corresponding to 1 X, 2 X and 4 X harmonics. The vibration amplitude of frequency scale level corresponding to 1 X may increase or decrease depending on the location of the crack and side load. However, the amplitude of frequency scale level corresponding to 2 X increases continuously as the crack propagates.

Laser–ultrasonic absorption measurements in low carbon steels

We have refined the contactless laser–ultrasound reverberation technique to measure ultrasonic absorption on small metallic samples. In this technique, a sample is supported by a holder which is ultrasonically decoupled from the sample. A pulsed laser is used to generate an acoustic pulse. After the pulse has mode converted and scattered sufficiently to fully insonify the sample, the decrease in the noise-like ultrasonic signal is recorded as a function of time using a laser-interferometer. A joint time–frequency analysis technique is used to extract an absorption spectrum from the signal. In this paper, the technique is demonstrated in a frequency bandwidth ranging from 1 to 7 MHz, and in a dynamic range of 0.003 to 0.3 dB μs −1. Measurements made on samples of three low-carbon steel grades, namely ultra low carbon (ULC), low carbon (LC), and high strength, low-alloy steels (HSLA), clearly show that ultrasonic absorption varies with steel grade. The technique was utilized to study the effect of a magnetic field on the ultrasonic absorption of an annealed ultra low carbon steel sample. It was found that magnetoelastic effects are responsible for a large fraction of the total absorption.

Aseismic designs based on artificial simulations

It is commonly believed that the time-varying frequency characteristics are important for the inelastic response of structures. Design code modification means structural cost changes; therefore any change must be based on solid evidence. Although the significance of time-varying frequency characteristics to ground motion has been recognized for a while, much more in-depth research is necessary before any meaningful engineering design conclusions can be reached. We apply joint time-frequency analysis techniques to a fully non-stationary ground motion model, with both intensity and frequency being non-stationary. The chirplet-based signal approximation is used to extract the time-varying frequencies from seismic ground-motion data samples. Based on that information, we compute the frequency-dependent modulating function in the stochastic model of the evolutionary random process. We generate and compare the artificial waves based on stochastic models of a uniform modulating random process and an evolutionary random process, separately. We also investigate the importance of the time-varying frequency characteristics of earthquake ground motion through an oscillator, with a single degree of freedom and elastic-plastic material behavior.

Wavelet and chaos analysis of flow induced vibration of a single cylinder in cross-flow

This paper attempts to shed some light on the characteristics of the flow induced vibration of an elastically mounted single cylinder in a cross flow using the newly developed joint time–frequency analysis techniques (JTFA) and in particular the modulated Gaussian wavelet. More specifically, the modulated Gaussian wavelet is chosen in the current work, since it was shown in [1]to be more effective than other well known wavelets in analysing the flow induced vibrations of a single cylinder similar to the one under consideration. In the current work, this wavelet shows good evidence that the flow induced vibration process, in the range of system parameters considered, is most likely to undergo nonstationary, nonlinear and chaotic dynamics. It is found that for certain values of system parameters the nonlinearity may lead the system into chaos. Phase plane portraits, Poincaré maps and fractal dimensions have been used to confirm the chaotic behavior of the response of the system under investigation.