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Showing papers on "Time–frequency analysis published in 2016"


Journal ArticleDOI
TL;DR: Results show that the EWT can provide a much higher resolution than the traditional continuous wavelet transform and offers the potential in precisely highlighting geological and stratigraphic information.
Abstract: Time–frequency analysis is able to reveal the useful information hidden in the seismic data. The high resolution of the time–frequency representation is of great importance to depict geological structures. In this letter, we propose a novel seismic time–frequency analysis approach using the newly developed empirical wavelet transform (EWT). It is the first time that EWT is applied in analyzing multichannel seismic data for the purpose of seismic exploration. EWT is a fully adaptive signal-analysis approach, which is similar to the empirical mode decomposition but has a consolidated mathematical background. EWT first estimates the frequency components presented in the seismic signal, then computes the boundaries, and extracts oscillatory components based on the boundaries computed. Synthetic, 2-D, and 3-D real seismic data are used to comprehensively demonstrate the effectiveness of the proposed seismic time–frequency analysis approach. Results show that the EWT can provide a much higher resolution than the traditional continuous wavelet transform and offers the potential in precisely highlighting geological and stratigraphic information.

194 citations


Journal ArticleDOI
TL;DR: In this paper, the rotor startup vibrations are utilized to solve the fault identification problem using time frequency techniques and numerical simulations are performed through finite element analysis of the rotor bearing system with individual and collective combinations of faults.

170 citations


Journal ArticleDOI
TL;DR: In this article, the authors proposed a method based on dynamic path optimization and fixed point iteration to find an appropriate ridge curve: a sequence of amplitude peak positions (ridge points), corresponding to the component of interest and providing a measure of its instantaneous frequency.

127 citations


Journal ArticleDOI
Gang Yu1, Yiqi Zhou1
TL;DR: In this paper, a general linear chirplet transform (GLCT) is proposed to characterize the signal of multi-component with distinct non-linear features, independent to the mathematical model and initial TFA method, allowing for reconstruction of the interested component, and being non-sensitivity to noise.

126 citations


Journal ArticleDOI
TL;DR: A technique using the generalized synchrosqueezing transform (GST) guided by enhanced TF ridge extraction is suggested to detect the existence of the bearing defects and results validate the effectiveness of the suggested technique for the bearing defect detection.
Abstract: Healthy rolling element bearings are vital guarantees for safe operation of the rotating machinery. Time-frequency (TF) signal analysis is an effective tool to detect bearing defects under time-varying shaft speed condition. However, it is a challenging work dealing with defective characteristic frequency and rotation frequency simultaneously without a tachometer. For this reason, a technique using the generalized synchrosqueezing transform (GST) guided by enhanced TF ridge extraction is suggested to detect the existence of the bearing defects. The low frequency band and the resonance band are first chopped from the Fourier spectrum of the bearing vibration measurements. The TF information of the lower band component and the resonance band envelope are represented using short-time Fourier transform, where the TF ridge are extracted by harmonic summation search and ridge candidate fusion operations. The inverse of the extracted TF ridge is subsequently used to guide the GST mapping the chirped TF representation to the constant one. The rectified TF pictures are then synchrosqueezed as sharper spectra where the rotation frequency and the defective characteristic frequency can be identified, respectively. Both simulated and experimental signals were used to evaluate the present technique. The results validate the effectiveness of the suggested technique for the bearing defect detection.

119 citations


Journal ArticleDOI
TL;DR: The main advantages of the proposed generalized stepwise demodulation transform for bearing condition monitoring under variable speed conditions include: (a) it can simultaneously improve energy concentration level of signals of interest and remove interferences in the TFR, (b) it is resampling-free and hence can avoid the resamplings related errors, and (c) it yields instantaneous frequencies for fault and shaft rotation and thus can carry out both fault detection and diagnosis tasks.

117 citations


Journal ArticleDOI
TL;DR: In this article, the authors proposed a time-frequency analysis method based on the Vold-Kalman filter and higher order energy separation (HOES) to extract fault symptoms for wind turbines.

116 citations


Journal ArticleDOI
TL;DR: Application of the VMD on field data demonstrates that instantaneous spectrum after VMD targets the thickness variation in the coal seam more sensitively than the conventional tools and highlights the fine details that might escape unnoticed.
Abstract: Seismic time–frequency analysis methods play an important role in seismic interpretation for its superiority in significantly revealing the frequency content of a seismic signal changes with time variation. Variational-mode decomposition (VMD) is a newly developed methodology for decomposition on adaptive and quasi-orthogonal signal and can decompose a seismic signal into a number of band-limited quasi-orthogonal intrinsic mode functions (IMFs). Each mode is an AM–FM signal with the narrow-band property and nonnegative smoothly varying instantaneous frequencies. Analysis on synthetic and real data shows that this method is more robust to noise and has stronger local decomposition ability than the empirical mode decomposition (EMD)-based methods. Comparing with the short-time Fourier transform (STFT) or wavelet transform (WT), instantaneous spectrum after VMD promises higher spectral and spatial resolution. Application of the VMD on field data demonstrates that instantaneous spectrum after VMD targets the thickness variation in the coal seam more sensitively than the conventional tools and highlights the fine details that might escape unnoticed. The technique is more promising for seismic signal processing and interpretation.

98 citations


Journal ArticleDOI
TL;DR: An effective fault-diagnosis strategy based on energy distributions variations of OLTC vibration signals according to Lorentz information measure is brought up and the calculated results under normal and typical fault conditions have shown that the energy spectrums of different conditions vary significantly so that the similarity index can measure the difference degree of energy distribution.
Abstract: The suitable condition of an on-load tap-changer (OLTC) is essential for the operation of converter transformer due to its frequent switch for the voltage regulation of power system. This paper describes a methodology to obtain the OLTC vibration characteristics in time–frequency domain. Considering the possible aliasing effect in vibration signal processing, an improved empirical mode decomposition (EMD) is proposed with masking signals of multiple frequencies added, which has obvious superiority in aliasing reduction compared with conventional methods. Then, an effective fault-diagnosis strategy based on energy distributions variations of OLTC vibration signals according to Lorentz information measure is brought up. The calculated results under normal and typical fault conditions of model and real OLTC have shown that the energy spectrums of different conditions vary significantly so that the similarity index can measure the difference degree of energy distribution. Meanwhile, the index of contact looseness is higher than the insulated panel looseness which indicates that the contact looseness fault has greater influence on switch-over process of OLTC.

82 citations


Journal ArticleDOI
TL;DR: In this article, the authors proposed an improvement with fine time-frequency resolution and free from interferences for highly nonstationary multi-component signals, by exploiting the merits of iterative generalized demodulation.

71 citations


Journal ArticleDOI
TL;DR: Simulation studies and applications to real EEG data elucidate that the proposed wavelet approach is capable of achieving a high time-frequency representation for nonstationary processes.

Journal ArticleDOI
TL;DR: In this article, a high-resolution adaptive directional time-frequency distribution (ADTFD) is defined by locally adapting the direction of its smoothing kernel at each t,f point.
Abstract: This paper presents a locally adaptive time-frequency t,f method for estimating the instantaneous frequency IF of multi-component signals. A high-resolution adaptive directional time-frequency distribution ADTFD is defined by locally adapting the direction of its smoothing kernel at each t,f point based on the direction of the energy distribution in the t,f domain. The IF of signal components is then estimated from the ADTFD using an image processing algorithm. Using the mean square error between the original IF and estimated IF as a performance criterion, experimental results indicate that the ADTFD gives better IF estimation performance compared with other TFDs for a multi-component signal. For example, for signal-to-noise ratio of 12dB, the IF estimate obtained using the ADTFD achieves a mean square error of -42dB for a weak signal component, which is an improvement of -12dB compared with other TFDs. Copyright © 2015John Wiley & Sons, Ltd.

Journal ArticleDOI
TL;DR: The proposed TFM sparse reconstruction method combines the merits of the TFM in denoising and the atomic decomposition in image sparse reconstruction and makes it possible to express the nonlinear signal processing results explicitly in theory.

Journal ArticleDOI
TL;DR: In this article, an iterative transient feature extraction approach is proposed based on time-frequency domain sparse representation, where the TF atoms are constructed based on the TF distribution (TFD) of the Morlet wavelet bases and local TF templates are formulated from TF atoms for the matching process.

Journal ArticleDOI
TL;DR: In this article, a harmonic wavelets based approximate analytical technique for determining the response evolutionary power spectrum of linear and non-linear (time-variant) oscillators endowed with fractional derivative elements is developed.
Abstract: A harmonic wavelets based approximate analytical technique for determining the response evolutionary power spectrum of linear and non-linear (time-variant) oscillators endowed with fractional derivative elements is developed. Specifically, time- and frequency-dependent harmonic wavelets based frequency response functions are defined based on the localization properties of harmonic wavelets. This leads to a closed form harmonic wavelets based excitation-response relationship which can be viewed as a natural generalization of the celebrated Wiener–Khinchin spectral relationship of the linear stationary random vibration theory to account for fully non-stationary in time and frequency stochastic processes. Further, relying on the orthogonality properties of harmonic wavelets an extension via statistical linearization of the excitation-response relationship for the case of non-linear systems is developed. This involves the novel concept of determining optimal equivalent linear elements which are both time- and frequency-dependent. Several linear and non-linear oscillators with fractional derivative elements are studied as numerical examples. Comparisons with pertinent Monte Carlo simulations demonstrate the reliability of the technique.

Journal ArticleDOI
TL;DR: The proposed method circumvents the need for minimum-phase transfer functions and is able to localize causality in time and frequency suitably, finding that financial stress has been causing economic activity particularly during the unwinding financial and economic distress and not the other way around.
Abstract: This paper proposes a continuous wavelet transform causality method that dispenses with minimum-phase spectral density matrix factorization. Extant methods based on minimum-phase function are computationally intensive and those utilizing discrete wavelet transform also fail to unfold causal effects over time and frequency. The proposed method circumvents the need for minimum-phase transfer functions and is able to localize causality in time and frequency suitably. We study the ability of the proposed method using simulated data and find that it performs excellently in identifying the causal islands. We then use the method to analyze the time---frequency causal effects in the relationship between the US financial stress and economic activity and find that financial stress has been causing economic activity particularly during the unwinding financial and economic distress and not the other way around.

Journal ArticleDOI
TL;DR: In this paper, an enhanced short-time Fourier transform (STFT)-based jamming mitigation system which employs several windows to increase time-frequency (t-f) plane resolution is proposed.
Abstract: In this study, an enhanced short-time Fourier transform (STFT)-based jamming mitigation system which employs several windows to increase time–frequency (t–f) plane resolution is proposed. A technique for employing near-optimum windows is also proposed. It is observed that selection of these windows leads to increasing the resolution of the t–f representation. An infinite impulse-response notch filter is employed for jamming removal process. Performance of the proposed method is evaluated by a set of measured global positioning system data. It is compared with conventional STFT-based algorithms reported in literature.

Journal ArticleDOI
TL;DR: In this article, an improved AM-FM decomposition algorithm is proposed for multivariate non-stationary and nonlinear data analysis, which is based on the EMD-based scalogram and coscalogram, and instantaneous frequency spectra and cospectra.
Abstract: Currently, empirical mode decomposition (EMD) has become a popular data-driven time-frequency analysis method for nonstationary and nonlinear data. However, it is still limited to univariate data due to the number and/or scale misalignment for multivariate data. A newly developed multivariate EMD (MEMD) scheme decomposes multivariate data simultaneously and thus leads to mode alignment and minimizes mode mixing. In addition, an improved amplitude and frequency modulation (AM-FM) decomposition algorithm presented here provides an estimation of a more meaningful instantaneous amplitude and frequency than the widely used Hilbert transform (HT). Both of these facilitate development of a time-frequency analysis framework for multivariate nonstationary and nonlinear data analysis. In this paper, MEMD-based scalogram and coscalogram, and instantaneous frequency spectra and cospectra are proposed to characterize a multivariate nonstationary process. The scalogram and instantaneous frequency spectra capture spectral evolution of each component while the coscalogram and instantaneous frequency cospectra reveal embedded intermittent correlation between two components. Compared with scale-based scalogram and coscalogram, frequency-based instantaneous frequency spectra and cospectra provide a more detailed portrait of multivariate data. The effectiveness of the proposed MEMD-based time-frequency analysis framework is demonstrated by numerical examples of a thunderstorm downburst and an earthquake ground motion. Also, the results from the MEMD-based approach are compared with those based on a continuous wavelet transform, which further reinforces the effectiveness of the proposed framework.

Journal ArticleDOI
TL;DR: MBD based time–frequency spectrum is able to provide the instantaneous variations of frequency components associated with fatiguing contractions and it is found that the values of IMDF, IMNF and InstSPR in LFB region have lowest variability across different subjects in comparison with other two features.

Journal ArticleDOI
TL;DR: In this paper, the combination of the Hilbert-Huang transform with the continuous wavelet transform (CWT) was used for the identification of localized corrosion in electrochemical noise signals.

Journal ArticleDOI
TL;DR: Two new approaches to mode reconstruction are discussed, the first determines the ridge associated with a mode by considering the location where the direction of the reassignment vector sharply changes, the technique used to determine the basin of attraction being directly derived from that used for ridge extraction.
Abstract: This paper discusses methods for the adaptive reconstruction of the modes of multicomponent AM-FM signals by their time-frequency (TF) representation derived from their short-time Fourier transform (STFT). The STFT of an AM-FM component or mode spreads the information relative to that mode in the TF plane around curves commonly called ridges. An alternative view is to consider a mode as a particular TF domain termed a basin of attraction. Here we discuss two new approaches to mode reconstruction. The first determines the ridge associated with a mode by considering the location where the direction of the reassignment vector sharply changes, the technique used to determine the basin of attraction being directly derived from that used for ridge extraction. A second uses the fact that the STFT of a signal is fully characterized by its zeros (and then the particular distribution of these zeros for Gaussian noise) to deduce an algorithm to compute the mode domains. For both techniques, mode reconstruction is then carried out by simply integrating the information inside these basins of attraction or domains.

Journal ArticleDOI
TL;DR: It is proved an easily checkable injectivity condition for recovery of any signal from all $$N^2$$N2 time-frequency shifts, and for Recovery of sparse signals, when only some of those measurements are given.
Abstract: Compressed sensing investigates the recovery of sparse signals from linear measurements. But often, in a wide range of applications, one is given only the absolute values (squared) of the linear measurements. Recovering such signals (not necessarily sparse) is known as the phase retrieval problem. We consider this problem in the case when the measurements are time-frequency shifts of a suitably chosen generator, i.e. coming from a Gabor frame. We prove an easily checkable injectivity condition for recovery of any signal from all \(N^2\) time-frequency shifts, and for recovery of sparse signals, when only some of those measurements are given.

Proceedings ArticleDOI
17 Jul 2016
TL;DR: In this paper, the application of Short Time Fourier Transform (STFT) to not only detect but also differentiate types of disturbance in HVDC system is investigated, and a parameter is proposed to capture the pattern of the side lobes in frequency domain.
Abstract: The protection of HVDC system is an important topic for stable transmission. When fault happens, DC breaker opens the circuit electronically unlike AC breaker, therefore, its operation is very fast. Besides that, it is also equally important to design a fast fault detection method. This paper investigates the application of Short Time Fourier Transform (STFT) to not only detect but also differentiate types of disturbance. For this application specifically, one needs to compromise frequency resolution in exchange for better time resolution. As a result, we are no longer able to find the frequency components in fault signal. To address that, a parameter is proposed to capture the pattern of the side lobes in frequency domain. It is seen that STFT, with that parameter, can effectively detect the DC fault very fast (<1.5ms), as well as differentiate it from AC fault and load change. A two-terminal Modular Multilevel Converter (MMC) HVDC system is used to simulate fault in PSCAD/EMTDC. The DC current is exported to MATLAB to perform STFT.

Proceedings ArticleDOI
23 Feb 2016
TL;DR: In this paper, a novel Fractional Fourier Transform (FrFT) based multiplexing scheme is presented as a joint radar-communication technique, which is used to embed data into chirp sub-carriers with different time-frequency rates.
Abstract: The increasing demand of spectrum resources and the need to keep the size, weight and power consumption of modern radar as low as possible, has led to the development of solutions like joint radar-communication systems. In this paper a novel Fractional Fourier Transform (FrFT) based multiplexing scheme is presented as a joint radar-communication technique. The FrFT is used to embed data into chirp sub-carriers with different time-frequency rates. Some optimisation procedures are also proposed, with the objective of improving the bandwidth occupancy and the bit rate and/or Bit Error Ratio (BER). The generated waveform is demonstrated to be robust to distortions introduced by the channel, leading to low BER, while keeping good radar characteristics compared to a widely used Linear Frequency Modulated (LFM) pulse with same duration and bandwidth.

Journal ArticleDOI
TL;DR: A TFM-based sparse signal reconstruction method combining time-frequency manifold (TFM) and sparse reconstruction for fault signature enhancement of rolling element bearings that has a valuable theoretical contribution on explicit expression of nonlinear signal processing results.
Abstract: Denoising based on signal reconstruction has been one of the most important tasks in signal processing for rolling element bearing fault diagnosis. This paper proposes a sparse signal reconstruction method combining time–frequency manifold (TFM) and sparse reconstruction for fault signature enhancement of rolling element bearings. TFM has good denoising performance for analyzing the defective bearing vibration signals. However, the amplitude information will be influenced by its nonlinear processing. This paper proposes employment of the sparse decomposition method to overcome this problem. The sparse decomposition is first conducted to process the TFM-based result on a designed overcomplete dictionary. Furthermore, the coefficients of the achieved sparse atoms are obtained by projecting the raw signal on the atoms to realize reconstruction of the bearing fault signature. The TFM-based sparse signal reconstruction method takes advantage of both TFM in denoising and the atomic decomposition in sparse reconstruction. The proposed method has a valuable theoretical contribution on explicit expression of nonlinear signal processing results. The results verified by experimental analysis indicate the value in fault signature enhancement of rolling element bearings and other mechanical movements.

Patent
11 May 2016
TL;DR: In this article, the authors present an alternative modulation scheme that maps data symbols intended for data transmission onto a symplectic-like 2D Fourier transform which operates on a form of the original data symbols.
Abstract: An alternative method of data communications using orthogonal time frequency shifting (OTFS) wireless waveforms configured so as to transmit data in a manner that is relatively insensitive to communications channel distortions and frequency shifts. In contrast to prior methods taught by applicant, the present disclosure teaches an alternative modulation scheme that maps data symbols intended for data transmission onto a symplectic-like 2D Fourier transform which operates on a form of the original data symbols. This 2D Fourier transform in turn is passed through a filter bank of narrow band filters, and the output in turn used to modulate transmitted waveforms according to various time slices until the entire 2D Fourier transform has been transmitted. At the receiver, and inverse of this process can be used to both characterize the data channel and correct the received signals for channel distortions, thus receiving a clear form of the original data symbols.

Journal ArticleDOI
TL;DR: Based on the characteristics of quadratic TFRs of PCCF signals, a detection and localization approach for contact wire irregularity is proposed in this paper and results indicate that the different forms of contact wire irregularities could be effectively detected and localized with meter accuracy using the Zhao-Atlas-Marks distribution.
Abstract: As for the rapid expansion of high-speed electrified railway industry, the current collection quality of electric locomotives becomes one of the crucial technical issues for the stable operation of trains. The key measurement index that can characterize the quality of current collection is the contact force between the catenary and pantograph on the locomotive roof. One of the major factors that affect the contact force is the contact wire irregularity, which appears to be very difficult to be measured or detected at present. In recent years, the frequency-domain indicators of the pantograph–catenary contact force (PCCF) have been gaining importance to address this issue. In this paper, a recent quadratic time–frequency distribution is selected to describe the time–frequency representation (TFR) of PCCF signal based on its nonstationarity. The Zhao–Atlas–Marks distribution (ZAMD) shows high time–frequency resolution and cross-term suppression for PCCF signals. To investigate the deterioration of the PCCF under contact wire irregularity, a pantograph–catenary interaction model is presented and verified. Some synthetic contact wire irregularities are generated and added to the interaction model. Based on the characteristics of quadratic TFRs of PCCF signals, a detection and localization approach for contact wire irregularity is proposed in this paper. The simulation and real-life experimental results indicate that the different forms of contact wire irregularity could be effectively detected and localized with meter accuracy using the ZAMD.

Journal ArticleDOI
TL;DR: A very simple and empirical technique based only on the analysis of local extremes from a modulated time sequence to find a new time sequence that carries the wanted relevant fault data and results demonstrate the effectiveness of the proposed technique.
Abstract: Signal demodulation is a fundamental procedure in many situations during a spectral analysis. Through an envelope analysis, it becomes possible to identify fault frequencies that are embedded in a modulated signal and that are not clearly visible only by directly applying some signal processing techniques such as the Fourier transform and filtering. In this paper, a very simple and empirical technique for demodulation is proposed. It is based only on the analysis of local extremes from a modulated time sequence to find a new time sequence that carries the wanted relevant fault data. The method of analysis is a good alternative tool for electrical fault detection in induction motors. The numerical and experimental results demonstrate the effectiveness of the proposed technique.

Journal ArticleDOI
TL;DR: A multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP) is described, and it is shown that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex.
Abstract: Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which have durations not exceeding tens of milliseconds. Further, brain signals are highly nonstationary because both behavioral state and external stimuli can change on a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal, something not always possible using standard signal processing techniques such as short time fourier transform, multitaper method, wavelet transform, or Hilbert transform. In this review, we describe a multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex. We compare the performance of MP with other techniques and discuss its advantages and limitations. Data and codes for generating all time-frequency power spectra are provided.

Journal ArticleDOI
TL;DR: In this paper, a new output-only system identification (SI) technique based on Time-Frequency Blind Source Separation (TFBSS) is proposed, which selectively utilizes effective information in local regions of the time-frequency plane, where only one mode contributes to energy, to identify mode shapes and recover modal responses from the non-stationary response where the traditional SI methods often encounter difficulties.