Short-time Fourier Transform Method Research Articles

An Experimental Study of the Effects of Cylinder Lubricating Oils on the Vibration Characteristics of a Two-Stroke Low-Speed Marine Diesel Engine

Abstract Two-stroke, low-speed diesel engines are widely used in large ships due to their good performance and fuel economy. However, there have been few studies of the effects of lubricating oils on the vibration of two-stroke, low-speed diesel engines. In this work, the effects of three different lubricating oils on the vibration characteristics of a low-speed engine are investigated, using the frequency domain, time-frequency domain, fast Fourier transform (FFT) and short-time Fourier transform (STFT) methods. The results show that non-invasive condition monitoring of the wear to a cylinder liner in a low-speed marine engine can be successfully achieved based on vibration signals. Both the FFT and STFT methods are capable of capturing information about combustion in the cylinder online in real time, and the STFT method also provides the ability to visualise the results with more comprehensive information. From the online condition monitoring of vibration signals, cylinder lubricants with medium viscosity and medium alkali content are found to have the best wear protection properties. This result is consistent with those of an elemental analysis of cylinder lubrication properties and an analysis of the data measured from a piston lifted from the cylinder after 300 h of engine operation.

Wind turbine blade damage detection and classification based on sound feature signal using machine learning

Wind turbine blades experience variable environmental conditions and are prone to failure due to fatigue and weather-induced damage. In this study, the aerodynamic sound signal of wind turbine blades is collected while in operation. Short-Time Fourier Transform method identified and used to analyze the periodic signal of the rotation of the wind turbine blades. The spectral centroid of the pulse signal and the variation of the corresponding three blades spectral centroid are extracted as the input features for supervised machine learning. Gaussian mixture model algorithm classified features and used with blades damage trend for structural health monitoring. Through the Gaussian mixture model established by using the mean value and standard deviation of the peak value of the spectral centroid, the results after the model test show the accuracy of the damaged blade judgment can reach 98 %. In addition, from the visual classification chart, it can be known that the blade will enter the damage state, and maintenance should be carried out in advance to avoid damage to the blade and maintain the stable operation of the wind turbine.

Time-Resolved Electrochemical Impedance Spectroscopy of Stochastic Nanoparticle Collision: Short Time Fourier Transform versus Continuous Wavelet Transform.

This work demonstrates the utilization of short-time Fourier transform (STFT), and continuous wavelet transform (CWT) electrochemical impedance spectroscopy (EIS) for time-resolved analysis of stochastic collision events of platinum nanoparticles (NPs) onto gold ultramicroelectrode (UME). The enhanced electrocatalytic activity is observed in both chronoamperometry (CA) and EIS. CA provides the impact moment and rough estimation of the size of NPs. The quantitative information such as charge transfer resistance (Rct ) relevant to the exchange current density of a single Pt NP is estimated from EIS. The CWT analysis of the phase angle parameter is better for NP collision detection in terms of time resolution compared to the STFT method.

Vibration Characteristics of Corn Combine Harvester with the Time-Varying Mass System under Non-Stationary Random Vibration

In field harvesting conditions, the non-stationary random vibration characteristics of the harvester are rarely considered, and the results of vibration frequency calculated by different time–frequency transformation methods are different. In this paper, the harvester’s vibration characteristics under the time-varying mass were studied, and the correlation between vibration frequency and modal frequency was analyzed. Firstly, under the conditions of time-varying mass (field harvesting conditions) and non-time-varying mass (empty running condition), the non-stationarity characteristics of vibration signals at 16 measurement points of a combined corn harvester frame were studied. Then, fast Fourier transform (FFT), short-time Fourier transform (STFT), and continuous wavelet transform (CWT) were used to calculate the vibration frequency distribution characteristics of the corn harvester. Finally, based on the EFDD (enhanced frequency domain decomposition) algorithm, the correlation between the primary vibration frequency and the operating mode frequency is studied. The results show that the mean, variance, and maximum difference of the vibration amplitude under harvesting conditions (mass time-varying system) are 0.10, 26.5, and 1.0, respectively, at different harvesting periods (0~10 s, 10~20 s, 20~30 s). The harvesting conditions’ vibration signals conform to the characteristics of non-stationary randomness. The FFT algorithm is used to obtain more dense vibration frequencies, while the frequencies based on STFT and CWT algorithms are sparse. The correlation between the FFT method and the EFDD algorithm is 0.98, and the correlation between the STFT, CWT, and the EFDD algorithm is 0.99 and 0.98. Therefore, the primary frequency of the STFT methods is closer to the modal frequency. Our research laid the foundation for further study and application of mass time-varying combined harvester system non-stationary random vibration modal frequency identification and vibration control.

Uterine Ultrasound Doppler Hemodynamics of Magnesium Sulfate Combined with Labetalol in the Treatment of Pregnancy-Induced Hypertension Using Empirical Wavelet Transform Algorithm.

The aim of this study was to explore the hemodynamic changes of magnesium sulfate combined with labetalol in the treatment of pregnancy-induced hypertension (PIH) under Doppler uterine ultrasound based on the empirical wavelet transform (EWT) algorithm. 500 patients with PIH in the hospital were selected and randomly divided into the control group (n = 250) and the observation group (n = 250). The control group was treated with conventional magnesium sulfate; the observation group was given labetalol based on magnesium sulfate drip in the control group. The uterine artery blood flow simulation model was established based on the EWT algorithm and compared with a short-time Fourier transform (STFT). The normalized root mean square error (NRMSE) of the STFT method was 0.19, and the NRMSE extracted by the EWT method was 0.13. After treatment, the blood pressure index, 24-hour urinary protein, and incidence of adverse birth outcomes in the observation group were lower than those in the control group; the effective rate of the control group (90.4%) was lower than that of the observation group (97.6%); the hemodynamic indexes of the uterine artery in the observation group were lower than those in the control group, and the differences were statistically significant (P < 0.05). The estimation accuracy of the EWT method was higher than that of the traditional STFT method; the combined treatment of magnesium sulfate and labetalol in patients with PIH had a remarkable effect, which could control the blood pressure index and reduce the 24-hour urinary protein; the uterine artery Doppler ultrasound examination could change hemodynamics and improve the adverse outcomes of mothers and infants.

Using A Sliding Window Phase Matching Method for Imaging of GNSS Radio Occultation Signals

Global Navigation Satellite System Radio Occultation (GNSS-RO) is a technique used to sound the atmosphere and derive vertical profiles of refractivity. Signals from GNSS satellites are received in a low-Earth orbit, and they are then processed to produce bending angle profiles, from which meteorological parameters can be retrieved. Generating two-dimensional images in the form of spectrograms from GNSS-RO signals is commonly done to, for instance, investigate reflections or estimate signal quality in the lower troposphere. This is typically implemented using, e.g., the Short-Time Fourier Transform (STFT) to produce a time-frequency representation that is subsequently transformed to bending angle (BA) and impact height (IH) coordinates by non-linear mapping. In this paper, we propose an alternative method based on a straightforward extension of the Phase Matching (PM) operator to produce two-dimensional spectral images in the BA-IH domain by applying a sliding window. This Sliding Window Phase Matching (SWPM) method generates the spectral amplitude on an arbitrary grid in BA and IH, e.g., along the coordinate axes. To illustrate, we show both SWPM and STFT methods applied to operational MetOp-A data. For SWPM we use a constant window in the BA-dimension, whereas for STFT we use a conventional constant time window. We show that the SWPM method produces the same result as STFT when the same window length is used for both methods. The sample points in impact parameter and bending angle are those generated by and the main advantage is that SWPM offers the user a convenient way to freely sample the BA-IH space. The cost for this is processing time that is somewhat longer than implementations based on the Fast Fourier Transform, such as the STFT method.

Application of the local maximum synchrosqueezing transform for seismic data

Seismic signal analysis is the main step in data processing through the petroleum exploration via costly seismic investigations. Precision of target delineation by seismic data for exploratory drilling strongly depends on resolution of the seismic image. However, resolution of the seismic image is restricted by the band-limited nature of the seismic signal and inherent deficiencies in signal enhancement and random noise attenuation methods. Generally, it is required to increase resolution of the seismic data both in time and frequency domains. General solution to achieve this goal is employing time – frequency transformation (TFT) methods. The most applicable and conventional TFT method is the short time Fourier transform (STFT). Nevertheless, the STFT method does not provide sufficient resolution for further seismological interpretation on data. Thus, reassignment and simultaneous synchrosqueezing transform methods were introduced that increase resolution in time by reassignment of coefficients to their true position. However, they are still not appropriate for obtaining required resolution in seismic signal. The local maximum synchrosqueezing transform (LMSST) was introduced as an efficient method for signal analysis. The LMSST method yet was not used for analysis of seismic data and the procedure of its parameters optimization for applying on seismic data is unclear. Therefore, in the presented study, we propose a strategy for defining optimum parameters of the LMSST method and the procedure of its application on seismic data, where high resolution seismic image is required for hydrocarbon exploration. The proposed strategy was applied on two synthetic data examples, considering the quality factor expression and contamination by random noise and a field data example from a natural gas reservoir. Result of applying the proposed strategy on synthetic and field data examples and comparison of results with the leading-edge methods, revealed that this strategy could be considered as an alternative to the common signal enhancement methods. Additionally, the proposed method consists of the advantage of easy implementation and full reconstruction of the original signal. It was also proved that the proposed strategy is a robust method against added random noise compared to other competitive methods when applied on nonstationary seismic signal.

A Comparison of Time-Frequency Methods for Real-Time Application to High-Rate Dynamic Systems

High-rate dynamic systems are defined as engineering systems experiencing dynamic events of typical amplitudes higher than 100 gn for a duration of less than 100 ms. The implementation of feedback decision mechanisms in high-rate systems could improve their operations and safety, and even be critical to their deployment. However, these systems are characterized by large uncertainties, high non-stationarities, and unmodeled dynamics, and it follows that the design of real-time state-estimators for such purpose is difficult. In this paper, we compare the promise of five time-frequency representation (TFR) methods at conducting real-time state estimation for high-rate systems, with the objective of providing a path to designing implementable algorithms. In particular, we examine the performance of the short-time Fourier transform (STFT), wavelet transformation (WT), Wigner–Ville distribution (WVD), synchrosqueezed transform (SST), and multi-synchrosqueezed transform (MSST) methods. This study is conducted using experimental data from the DROPBEAR (Dynamic Reproduction of Projectiles in Ballistic Environments for Advanced Research) testbed, consisting of a rapidly moving cart on a cantilever beam that acts as a moving boundary condition. The capability of each method at extracting the beam’s fundamental frequency is evaluated in terms of precision, spectral energy concentration, computation speed, and convergence speed. It is found that both the STFT and WT methods are promising methods due to their fast computation speed, with the WT showing particular promise due to its faster convergence, but at the cost of lower precision on the estimation depending on circumstances.

Multiple Source of Microseismic Signal Classification by Adaptive Short-time Fourier Transform Method

Multiple Source of Microseismic Signal Classification by Adaptive Short-time Fourier Transform Method

The Detection of Freezing of Gait in Parkinson's Disease Using Asymmetric Basis Function TV-ARMA Time-Frequency Spectral Estimation Method.

Freezing of gait (FOG) is an episodic gait disturbance affecting locomotion in Parkinson's disease. As a biomarker to detect FOG, the Freeze index (FI), which is defined as the ratio of the areas under power spectra in 'freeze' band and in 'locomotion' band, can negatively be affected by poor time and frequency resolution of time-frequency spectrum estimate when short-time Fourier transform (STFT) or Wavelet transform (WT) is used. In this study, a novel high-resolution parametric time-frequency spectral estimation method is proposed to improve the accuracy of FI. A time-varying autoregressive moving average model (TV-ARMA) is first identified where the time-varying parameters are estimated using an asymmetric basis function expansion method. The TV-ARMA model is then transformed into frequency domain to estimate the time-frequency spectrum and calculate the FI. Results evaluated on the Daphnet Freezing of Gait Dataset show that the new method improves the time and frequency resolutions of the time-frequency spectrum and the associate FI has better performance in the detection of FOG than its counterparts based on STFT and WT methods do. Moreover, FOGs can be predicted in advance of its occurrence in most cases using the new method.

Moving Ground Target Detection With Seismic Signal Using Smooth Pseudo Wigner–Ville Distribution

The main challenging task in moving ground target detection with seismic signals is the extraction of robust feature vectors for accurate localization of a seismic event. Toward this goal, a new feature extraction technique is proposed for seismic event detection based on time-frequency analysis implemented with the Smooth pseudo Wigner–Ville distribution (SPWVD). The time-frequency coefficients are utilized to constitute a Renyi entropy vector exploiting a probability distribution function. This entropy vector gives information on the localized measurement of a possible seismic event resulting from moving ground vehicles. The likelihood of an actual seismic event is then ensured by using a constant false alarm rate (CFAR) detector which dynamically adjusts the threshold to minimize false alarms. The proposed algorithm is tested on the benchmarked data set SITEX02 consisting of seismic signals generated by moving tracked and wheeled vehicles. Additionally, the algorithm is also applied on a data set generated by collecting the seismic signature of a moving wheeled vehicle, i.e., bus within the campus. The results are also compared with pseudo Wigner–Ville distribution (PWVD) and short-time Fourier transform (STFT)-based seismic event detection technique. Significantly improved detection results are observed for SPWVD technique. An F-score improvement of ~24% as well as lead time enhancement of twofold is observed in comparison with the PWVD for relatively weak seismic signals generated by wheeled Dragon Wagon (DW) vehicles. Similarly, the F-score improvement is of ~13% and the lead enhancement is approximately 10 s in comparison with the STFT method.

An Effective Detection Method of Voltage and Frequency Fluctuations Based on a Combination of TEO/DESA and STFT Analysis

Nowadays, it is a very essential issue to detect and recognize power quality (PQ) disturbances as soon as possible, because it can give very important information by which various kinds of power quality mitigation devices can respond for each phenomenon quickly and to capture the interested waveform parts instantaneously, and so on. Also, it can be widely used as a trigger or reference signal for data mining to determine the existence of anomalies in the power system. We have studied some applications of Teager Energy Operator (TEO) and its adjunctive techniques known as Discrete Energy Operation Algorithm (DESA) to classify and detect the waveform distortions such as harmonics, inter-harmonics and frequency variation and the voltage synchronization. This paper is written to propose an effective detection method of voltage and frequency fluctuation based on TEO/DESA combined with Short-Time Fourier Transform (STFT), which is designed to overcome the drawback of the former study, a combined TEO and STFT method.

Widely Linear Adaptive Instantaneous Frequency Estimation in Vector Hydrophones

To solve the problem that the time-frequency resolution of Short-Time Fourier Transform (STFT) is constrained by the window length and the moving step of the short time window, and to utilize the merits of a widely linear method, a novel instantaneous frequency estimation method in vector hydrophone was proposed. In this paper, a complex variable was constructed. It is composed of sound pressure and particle velocity as its real part and imaginary part, respectively. The constructed variable was approved to be second order noncircular (improper). For the modelling of noncircular signals, the standard linear estimation is not adequate and the pseudo-covariance matrix should also be taken into consideration. As a result, a widely linear adaptive instantaneous frequency estimation algorithm and its three solutions based on the augmented complex least mean square (ACLMS) method are presented to estimate the instantaneous frequency in vector hydrophones. The results of simulations and laboratory experiments prove that this approach based on a widely linear model performs better compared to STFT and strict linear filter methods.

SPECTRAL DECOMPOSITION TECHNIQUE BASED ON STFT AND CWT FOR IDENTIFYING THE HYDROCARBON RESERVOIR

The spectral decomposition is one of the advanced interpretation techniques such as seismic inversion, amplitude versus offset analysis, and seismic attribute that helpful in direct interpretative approach in seismic exploration. This technique is a transformation algorithm, thus a signal can be transformed into its varying frequency contained in the seismic signal. There are a variety of spectral decomposition algorithms in the decomposing seismic signal from time domain into frequency domain. These algorithms include Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). The STFT algorithm is a conventional and simple technique for computing a time-frequency spectrum, which is based on the application of Fourier transform. However, the STFT algorithm has a problem related to the frequency resolution. In its implementation, this algorithm is limited by predefi ned window length. In contrast, the CWT algorithm is believed to be able to overcome the limitation of window length. The CWT threats wavelet at certain window length, which is defi ned by the characteristics of the wavelet. In this study, the comparison between spectral decomposition technique based on STFT and CWT method was performed, particularly in its application to the synthetic and real data set. Each algorithm has its own advantages and disadvantages in decomposing the seismic signal. Further, this analysis can be used as a reference to select one of two algorithms for the specifi c application. The synthetic data set application shows that CWT algorithm produces better frequency resolution compared to STFT algorithm. In addition, the real data set application shows that time frequency section of the seismic line provides a spectral feature, which is useful to identify the hydrocarbon reservoir, which is associated with low-frequency shadow zone.

Time-frequency analysis on gong timor music using short-time fourier transform and continuous wavelet transform

Time-Frequency Analysis on Gong Timor Music has an important role in the application of signal-processing music such as tone tracking and music transcription or music signal notation. Some of Gong characters is heard by different ways of forcing Gong himself, such as how to play Gong based on the Player’s senses, a set of Gong, and by changing the tempo of Gong instruments. Gong's musical signals have more complex analytical criteria than Western music instrument analysis. This research uses a Gong instrument and two notations; frequency analysis of Gong music frequency compared by the Short-time Fourier Transform (STFT), Overlap Short-time Fourier Transform (OSTFT), and Continuous Wavelet Transform (CWT) method. In the STFT and OSTFT methods, time-frequency analysis Gong music is used with different windows and hop size while CWT method uses Morlet wavelet. The results show that the CWT is better than the STFT methods.

Accurate Interference Mitigation in Global Positioning System Receivers Based on Double-Step Short-Time Fourier Transform

The impact of low-power interferences on the performance of global positioning system receivers is ignored in most literature. Low-power interferences degrade the positioning accuracy as well as disrupting the performance of the receiver in some situations. Since the resolution of short-time Fourier transform (STFT)-based jamming mitigation methods is insufficient for both low- and high-power interference mitigation, this paper proposes an interference mitigation method which employs two windows to increase time–frequency (t–f) plane resolution. In this paper, we also propose a new technique to select near-optimum windows in order to increase resolution of the t–f representation. The proposed technique results in significant increase in signal-to-interference-plus-noise power ratio improvement, the number of acquired satellites, and the positioning accuracy as well as considerable decrease in mean square error of the retrieved signal compared to standard STFT method.

A novel numerical mapping method based on entropy for digitizing DNA sequences

Recently, digital signal processing has been widely applied in the study of genomics. One of the genomic studies is identification of protein-coding regions. Where is a protein coded? How much is encoded? Where are growth and development regulated? The answer to these questions is possible by DNA sequences that can be classified as the exon and intron. In signal processing application, numerical signals are used due to symbolic signal nature of DNA sequence; yet, it must be converted from symbolic sequence to numeric sequence prior the analysis in data preprocessing. The bases in a DNA sequence are represented with four letters A, G, C and T. Each letter corresponds to a numeric value. In the literature, several numerical mapping techniques exist. In this paper, a novel numerical mapping approach has been proposed for converting string to numerical values. Each codon is mapped by improved fractional derivative of Shannon equation in this approach. For exon regions prediction, three methods have been used. These methods are singular value decomposition (SVD), discrete Fourier transform (DFT) and short-time Fourier transform (STFT). The performance of the proposed mapping technique has been evaluated based on the above-mentioned three classification methods. The proposed novel technique has showed more success in the identification of protein-coding regions as compared to the predominant existing mapping techniques SVD, DFT and STFT methods.

Frequency Dispersion Compensation Through Variable Window Utilization in Time-Reversal Techniques for Electromagnetic Waves

In microwave imaging applications, propagating signals undergo additional attenuation in dispersive and lossy media compared to the nondispersive and lossless media. In this communication, we introduce a threshold approach and short-time Fourier transform (STFT)-based inverse filters to compensate for such additional attenuation in time-reversal (TR)-based imaging algorithms. The method introduced here is utilized to reduce the unwanted noise amplification in the received signals during the compensation stage. Additionally, optimum settings for window type and length in the STFT method are obtained through a scanning operation in the propagation medium. Different window types and lengths are studied to achieve the best focusing resolution in TR applications. While utilizing a large number of windows with short spatial lengths provides an improved TR focusing performance, it also increases the overall cost and complexity of the imaging system. The threshold method introduced here achieves improved TR focusing performance without increasing the cost by utilizing a lower number of inverse filters.

Vibration-based Fault Diagnostic of a Spur Gearbox

This paper presents comparative studies of Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) as several advanced time-frequency analysis methods for diagnosing an early stage of spur gear tooth failure. An incipient fault of a chipped tooth was investigated in this work using vibration measurements from a spur gearbox test rig. Time Synchronous Averaging was implemented for the analysis to enhance the clarity of fault feature from the gear of interest. Based on the experimental results and analysis, it was shown that FFT method could identify the location of the faulty gear with sufficient accuracy. On the other hand, Short Time Fourier Transform method could not provide the angular location information of the faulty gear. It was found that the Continuous Wavelet Transform method offered the best representation of angle-frequency representation. It was not only able to distinguish the difference between the normal and faulty gearboxes from the joint angle-frequency results but could also provide an accurate angular location of the faulty gear tooth in the gearbox.

Time–Frequency Domain Analysis of Asphalt Longitudinal Strain

The application of time–frequency domain analysis methods, such as continuous wavelet transform (CWT) and short-time Fourier transform (STFT), is evaluated in the extraction of the dominant frequency from asphalt longitudinal strain signals. The pavement response data collected at the fully instrumented Integrated Road Research Facility in Edmonton, Alberta, Canada, was used in this study. Promising results were achieved when CWT and STFT were used to determine the dominant frequency of the measured longitudinal strain at the bottom of the asphalt layer at different vehicular speeds. The dominant frequencies obtained from these methods were compared with those from conventional time-to-frequency conversion methods. Results showed that a frequency calculation that used the inverse of tensile pulse duration led to noticeably larger frequencies compared with those associated with the CWT and STFT methods. The accuracy of the determined frequencies and the corresponding moduli of hot-mix asphalt were evaluated by predicting strains with the KENPAVE program. This analysis showed the advantage of using time–frequency domain methods because they led to more reasonable agreements between the measured and predicted responses. Finally, the impact of frequency calculation methods on the potential fatigue cracking life was assessed by taking the estimated moduli and strains of hot-mix asphalt into account. It was found that the fatigue cracking life can be overestimated by almost 45% when frequency is considered as a reciprocal of pulse duration compared with when prediction is based on time–frequency domain methods.