Short Time Fast Fourier Transform Research Articles

Recursive Use of the Short-Time Fast Fourier Transform for Signature Analysis in Continuous Processes

Recursive Use of the Short-Time Fast Fourier Transform for Signature Analysis in Continuous Processes

A Coherent on Receive X-Band Marine Radar for Ocean Observations.

Marine radars are increasingly popular for monitoring meteorological and oceanographic parameters such as ocean surface wind, waves and currents as well as bathymetry and shorelines. Within this paper a coherent on receive marine radar is introduced, which is based on an incoherent off the shelf pulsed X-band radar. The main concept of the coherentization is based on the coherent on receive principle, where the coherence is achieved by measuring the phase of the transmitted pulse from a leak in the radar circulator, which then serves as a reference phase for the transmitted pulse. The Doppler shift frequency can be computed from two consecutive pulse-pairs in the time domain or from the first moment of the Doppler spectrum inferred by means of a short time Fast Fourier Transform. From the Doppler shift frequencies, radial speed maps of the backscatter of the ocean surface are retrieved. The resulting backscatter intensity and Doppler speed maps are presented for horizontal as well as vertical polarization, and discussed with respect to meteorological and oceanographic applications.

Power quality assessment using signal periodicity independent algorithms – A shipboard microgrid case study

Retrofitting of shipboard microgrids is receiving much attention nowadays due to the flexibility it offers to adapt existing ships with rapid market variations to move towards all-electric ships (A-ESs). This modernity mainly relies on incorporating the power electronics converters that unfortunately draw a large number of harmonics, which affect the energy quality and threaten the system stability and crew/passengers safety. The contribution of this paper lies in proposing two developed open-loop algorithms to assess the harmonics distortion of A-ESs without relying on signal periodicity. The first algorithm is an offline-based analyzing technique developed for the short-term protective action stage, which incorporates the eigenvalue solution inside the short-time fast Fourier transform (ST-FFT) to adapt its window size. Consequently, this algorithm can provide a comprehensive harmonics analysis with a fast transient response even under large system frequency drifts. The second algorithm is an online-based assessing technique, which is very suitable for the long-term preventive action stage to provide accurate harmonics distortion assessment with the fast transient response and efficient computation burden. This approach relies on cascading three moving average filters (MAFs) with particular window sizes to filter and estimate the mean value inside the discrete Fourier transform and the true RMS blocks without the need of frequency information. Hence, it results in enhancing the harmonics rejection capability of the algorithm even under the existence of non-characteristic harmonics and frequency drifts. Simulation and experimental results are provided to validate the efficacy of the proposed algorithms and the results are compared with traditional methods.

Analysis and Modeling of Conducted EMI from an AC–DC Power Supply in LED TV up to 1 MHz

Critical conduction mode (CRM) boost power factor correction (PFC) converter is widely used in ac–dc power supplies to achieve high power factor. The switching frequency varies in a half-line cycle. In this article, both the differential-mode (DM) and common-mode (CM) electromagnetic interference (EMI) noises below 1 MHz from the ac–dc power supply in a LED TV are analyzed and modeled. The power supply consists of two parts: CRM boost PFC converter and LLC resonant converter. The conducted EMI noise and noise source voltages are measured in the time domain and then converted to the frequency domain via short-time fast Fourier transform (STFFT). Through the joint time-frequency analysis using STFFT, the drain-to-source voltage of the power MOSFET in the PFC converter is identified as the dominant noise source of both CM and DM EMI noises below 1 MHz from the power supply. The EMI current path is different during different periods of a cycle of the line voltage. During most time of a cycle, two diodes of the bridge rectifier are forward biased, and the bridge rectifier can be treated as short circuit. The current paths of DM and CM EMI noises are explained and modeled by a linear equivalent circuit model. Three parasitic capacitances need to be considered to model the CM EMI noise in this device under test. From the circuit model, transfer functions relating DM and CM EMI spectra to noise source voltage spectrum are obtained. The conducted EMI spectra are predicted by multiplying the spectrum of noise source voltage by the transfer functions. The prediction matches with measurement. The effects of parasitic capacitances on CM EMI noise are analyzed by simulation and then validated by measurement. The analysis results can help with EMI design to reduce the CM EMI.

On Vibration Joint Time-Frequency Investigations of CNC Milling Machines for Tool Trajectory Task Conformity Estimation

This study deals with estimation of milling shape accuracy and trajectory conformity for small CNC 3D milling machines, based on vibration monitoring during the regular working cycles. The author made a large number of experimental tests, acquiring the acceleration signals, both on the milling tool-holder and on the bed frame. In order to evaluate the appropriate spectral characteristics of different machine parts and their weights on equipment dynamics, it was analyzed both the complete and the partial working cycle (such as forward tool motion, with or without effective milling, with or without tool driving, exclusively the milling cutter transitory/stabilized regime) for different basic milling shapes. The acceleration signals were jointly time-frequency investigated in order to evaluate specific spectral indicators related to the real motion characteristic of milling tool. It was used short time fast Fourier transform (STFFT) and Hough transform (HT) algorithms, along with stochastic evaluation of signal parameters, within time and frequency domains. The results reveal an accurate correlation between the specific transitory dynamics of the machine and the imposed milling shape. Main implications of the results within this analysis involve the noninvasive and facile investigation for milling errors of the CNC machine, conformity of tool head trajectory, identification of potential failure source, or damaged machine part.

Time‐domain virtual EMI receiver model algorithm for corona‐originated electromagnetic interference of dc transmission line

A time-domain virtual electromagnetic interference (EMI) receiver model algorithm for corona-originated EMI of dc transmission line is proposed. The presented model is based on short-time fast Fourier transform (FFT) and digital quasi-peak, peak and average detector algorithms together with the measured corona current. The virtual EMI receiver model algorithm is verified by comparing with measurement results of conducted interference of the three regular waveforms. The influences of computational accuracy on the conducted interference are discussed. Through utilising the laboratory-scale corona cage to generate corona current, the 0.5 MHz excitation current is calculated by using short-time FFT and excitation function theory. The measured and computational average value, root mean square value, quasi-peak value and peak value of EMI receiver response to corona current sequence are obtained. It is found that computations of radio interference have a good agreement with the measured results.

Skin-Spar Failure Detection of a Composite Winglet Using FBG Sensors

Abstract Winglets are introduced into modern aircraft to reduce wing aerodynamic drag and to consequently optimize the fuel burn per mission. In order to be aerodynamically effective, these devices are installed at the wing tip section; this wing region is generally characterized by relevant oscillations induced by flights maneuvers and gust. The present work is focused on the validation of a continuous monitoring system based on fiber Bragg grating sensors and frequency domain analysis to detect physical condition of a skin-spar bonding failure in a composite winglet for in-service purposes. Optical fibers are used as deformation sensors. Short Time Fast Fourier Transform (STFT) analysis is applied to analyze the occurrence of structural response deviations on the base of strain data. Obtained results showed high accuracy in estimating static and dynamic deformations and great potentials in detecting structural failure occurrences.

Improving spatio-temporal resolution of infrared images to detect thermal activity of defect at the surface of inorganic glass

This paper proposes a noise suppression methodology to improve the spatio-temporal resolution of infrared images. The methodology is divided in two steps. The first one consists in removing the noise from the temporal signal at each pixel. Three basic temporal filters are considered for this purpose: average filter, cost function minimization (FIT) and short time Fast Fourier Transform approach (STFFT). But while this step effectively reduces the temporal signal noise at each pixel, the infrared images may still appear noisy. This is due to a random distribution of a residual offset value of pixels signal. Hence in the second step, the residual offset is identified by considering thermal images for which no mechanical loading is applied. In this case, the temperature variation field is homogeneous and the value of temperature variation at each pixel is theoretically equal to zero. The method is first tested on synthetic images built from infrared computer-generated images combined with experimental noise. The results demonstrate that this approach permits to keep the spatial resolution of infrared images equal to 1 pixel. The methodology is then applied to characterize thermal activity of a defect at the surface of inorganic glass submitted to cyclic mechanical loading. The three basic temporal filters are quantitatively compared and contrasted. Results obtained demonstrate that, contrarily to a basic spatio-temporal approach, the denoising method proposed is suitable to characterize low thermal activity combined to strong spatial gradients induced by cyclic heterogeneous deformations.

A Software-Defined Radio Based Adaptive HRPT/APT Signal Receiver

The paper discusses the design, implementation, and test of a software-defined radio (SDR) based receiver to acquire HRPT (high resolution picture transmission) and APT (automatic picture transmission) signals of NOAA weather satellites simultaneously, recognizes modulation types, and demodulates the signals. The captured HRPT RF signal is downconverted to the 137.5 MHz band in the RF (radio frequency) front-end and then combined with the APT RF signal. The combined signals are then downconverted and digitized at the programmable receiver platform. The digitized data are spectrally analyzed with short-time FFT (fast Fourier transform) to obtain an instantaneous spectrum for adjustment of the adaptive digital band-pass filter. The modulation recognition block identifies split-phase PSK (phase shift keying) for HRPT and FM (frequency modulation) for APT. The HRPT signal is then demodulated with a Costas loop because the two side bands have more power than the carrier. The APT signal is demodulated in an FM demodulator with Zoom-FFT and automatic gain control (AGC) to maintain the brightness of the whole weather map according to the brightness of the synchronization mark. The system functions are developed and realized in an SDR software platform. The resulting SDR-based receiver is then used to receive and process real signals.

Experimental design and signal selection for construction of a robot control system based on EEG signals

Abstract Aiming to develop an noninvasive BMI control system with EEG (electroencephalogram) signals to control external devices such as prostheses and robots for rehabilitation and/or power support, four different tasks corresponding to different brain excitation degrees are designed. Their EEG spectra are analyzed with short-time fast Fourier transform (STFFT), and their features of mu and beta rhythms corresponding to the different tasks are extracted. The extracted features are used to control a one-joint robot arm and their corresponding results are compared. The results show that the EEG signal when a subject is holding a weight is comparatively more stable than the EEG signals in other tasks such as motor imagery. This implies an effective way for power assist by EEG signals.

Multi-sensor couplers and waveguides for efficient detection of acoustic emission behavior of snow

The efficient detection of acoustic emission (AE) activity from snow having porous and fragile character is hindered by the fact that the AE signals have small amplitudes and are typically attenuated within a short distance from the source. We therefore tested seven different types of highly sensitive resonant AE sensors and a multi-channel AE system in a wide frequency range of 1kHz–400kHz to evaluate the performance of multi-sensor coupler and waveguides with varying displacement rates. The AE generated during small fracturing of a natural snowpack, caused by a ram penetrometer, were detected using a cylindrical waveguide and a detection range of up to 16m with detection efficiency of 38dBAE were observed in snow. The AE activities produced by the snowpack were continuously recorded using a 2D-arrestor for AE in relation with the natural melt-freeze process. Prominent AE activity was observed near the phase-transition temperature of snow. Furthermore, spectral analysis of the AE signals generated by snow was carried out using the Short-time Fast Fourier Transform (ST-FFT) method. The AE behavior of snow was observed at different states of stress by varying from low to high, corresponding to different frequency ranges and grain sizes and an empirical relation was established for peak AE rates as a function of displacement rate. Apart from it, an attempt was made to monitor the real time failure of snow sample vis-à-vis AE energy with the help of an AE system and a high-speed camera. Our results are quite encouraging towards application of AE technique in the direction of slope stability evaluation and AE-based non-destructive evaluation of snow under various physical processes. The network of acoustic arrestors and waveguides can be crucial towards prediction of slope stability in view of avalanche release.

Characterization of Failure Modes in Composite Laminates Under Flexural Loading Using Time–Frequency Analysis

Fiber reinforced composite materials are used as structural material in airplanes because of their high specific stiffness and strength. When composite materials are subjected to mechanical loading, it leads to many types of failures such as matrix cracking, debonding, delamination, and fiber breakage. To create a better understanding of the initiation, growth and interaction of the different types of damage, damage monitoring during mechanical loading is very important. Acoustic emission is a suitable technique for the detection of a wide range of micro-structural failures in composite materials. In this present paper, GFRP composite laminates with different stacking sequences such as [0°]4, [90°]4, angle ply [± 45°]4 and cross ply [0°/90°]4 are used to trigger different failure mechanisms when subjected to flexural test (three-point bending) with AE monitoring. Discrimination of the failure modes are done based on the predominance of the failure modes in each orientation. Parametric plots are used to discriminate the modes of fracture within the laminates. Range of frequency content in each orientation is investigated using fast Fourier transform (FFT) analysis. FFT enables calculating the frequency content of each damage mechanism. Short Time FFT highlights the possible failure mechanisms associated with each signal. Continuous wavelet transform allowed identifying frequency range and time history for failure modes in each signal. The predominance of failure modes in each orientation is used as a key in the study of discrimination of failure modes in composite laminates.

CONDUCTED EMISSION MEASUREMENT OF A CELL PHONE PROCESSOR MODULE

This paper discusses a conducted emission measurement of a cell phone integrated circuit. The industry standard measurement method is used to compare the measurement result to the deflned limit line. A data analysis method- short time fast Fourier transform (STFFT) is presented to help to analyze the result. The data consistency and repeatability is also analyzed.

Identification of Failure Modes in Composites from Clustered Acoustic Emission Data Using Pattern Recognition and Wavelet Transformation

Acoustic emission (AE) is widely used to characterize damage occurring in composite materials: however, the discrimination between AE signatures due to different damage mechanisms is still an open issue. In this study, the various failure mechanisms in bidirectional glass/epoxy laminates subjected to uni-axial tension are identified using AE monitoring. AE data recorded during the tensile testing of a single-layer specimen are used to identify matrix cracking and fiber failure. In contrast, delamination signals are characterized using a two-layer specimen with a pre-induced defect, produced by artificially inserting a 10 mm wide Teflon tape in the middle portion of the two layers. Twelve-layer Glass fiber reinforced plastics laminates were also tested as a reference for the comparison of results. The procedure leading to signal discrimination involves a number of steps. First, Fuzzy C-means clustering associated with principal component analysis are used to discriminate between failure mechanisms, while parametric studies using AE count rate and cumulative counts allowed damage discrimination at various stages of loading. The two above methods led to AE waveform selection: on the selected waveforms, fast Fourier transform (FFT) enabled calculating the frequency content of each damage mechanism. Continuous wavelet transform (WT) allowed identifying frequency range and time history for failure modes in each signal, while noise content associated with the different failure modes is calculated and removed by discrete WT. Short time FFT (STFFT) finally highlighted the possible failure mechanism associated with each signal.

Effect of fuzzy C means technique in failure mode discrimination of glass/epoxy laminates using acoustic emission monitoring

Acoustic emission is one of the powerful techniques that can be used for in situ structural health monitoring of composite laminates. One of the main issues of AE is to characterize the different damage mechanisms from the detected AE signals. Unsupervised Pattern recognition has been one of the techniques used for the identification of a specific failure mode in composites from Acoustic emission data. Cross ply composite laminate of size 300 × 300 mm is fabricated using Vacuum bag molding. ASTM D3039 Standard tensile specimens are cut from the laminate and these specimens are subjected to uni axial tensile test under Acoustic Emission monitoring. Fast Fourier transform analysis (FFT) and Short Time Fast Fourier Transform (STFFT) analysis are performed on the Wave forms of the AE hit data obtained during the conduct of tensile test to characterize the failure modes in crossply specimens. Fast Fourier Transform enabled calculating the frequency content of each damage mechanism. In this paper Fuzzy C Mean clustering is performed for the AE parameters obtained from the test and the efficiency of this technique is being investigated using FFT AND STFFT analysis.

A Global Method for the Identification of Failure Modes in Fiberglass Using Acoustic Emission

Abstract The various failure mechanisms in bidirectional glass/epoxy laminates loaded in tension are identified using acoustic emission (AE) analysis. AE data recorded during the tensile testing of a single layer specimen are used to identify matrix cracking and fiber failure, while delamination signals are characterized using a two-layer specimen with a pre-induced defect. Parametric studies using AE count rate and cumulative counts allowed damage discrimination at different levels of loading and Fuzzy C-means clustering associated with principal component analysis were used to discriminate between failure mechanisms. The two above methods led to AE waveform selection: On selected waveforms, Fast Fourier Transform (FFT) enabled calculating the frequency content of each damage mechanism. Continuous wavelet transform allowed identifying frequency range and time history for failure modes, whilst noise content associated with the different failure modes was calculated and removed by discrete wavelet transform. Short Time FFT finally highlighted the possible failure mechanism associated with each signal.

뇌전기파 분석용 FFT 프로세서 설계

본 논문에서는 의료 서비스를 위한 뇌전기파(EEG: electroencephalogram) 신호 분석용 FFT(Fast Fourier Transform) 프로세서를 구현하였다. 실시간으로 발생하는 EEG 신호를 블록으로 나누어 short-time FFT 처리하기 위해 Hamming 창 함수를 사용하였으며, 이로 인해 감소되는 양끝의 값은 1/2 오버랩 시켜 보완하였다. 0~100 [Hz] 사이의 주파수 특성을 갖는 뇌전기파의 효율적인 대역 분석을 위해 256-point FFF 프로세서를 radix-4 알고리듬을 적용하여 구현하였으며, 단일 메모리 뱅크 구조를 사용하여 집적도를 높였다. 설계된 FFT 프로세서는 FPGA 구현을 통해 가능을 검증하였으며, 연산오차가 2% 이내로 높은 연산 정밀도를 갖는다. This paper describes a design of fast Fourier transform(FFT) processor for EEG(electroencephalogram) signal analysis for health care services. Hamming window function with 1/2 overlapping is adopted to perform short-time FFT(ST-FFT) of a long period EEG signal occurred in real-time. In order to analyze efficiently EEG signals which have frequency characteristics in the range of 0 Hz to 100 Hz, a 256-point FFT processor is designed, which is based on a single-memory bank architecture and the radix-4 algorithm. The designed FFT processor has been verified by FPGA implementation, and has high accuracy with arithmetic error less than 2%.

Convolution-Based Trigonometric Interpolation of Band-Limited Signals

This paper presents a method to obtain a trigonometric polynomial that accurately interpolates a given band-limited signal from a finite sequence of samples. The polynomial delivers accurate approximations in the range covered by the sequence, except for a short frame close to the range limits. Besides, its accuracy increases exponentially with the frame width. The method is based on using a band-limited window in order to reduce the truncation error of a convolution series. It is shown that the polynomial can be efficiently constructed and evaluated using algorithms designed for the discrete Fourier transform (DFT). Specifically, two basic procedures are presented, one based on the fast Fourier transform (FFT), and another based on a recursive update algorithm for the short-time FFT. The paper contains three applications. The first is a variable fractional delay (VFD) filter, which consists of a short-time FFT combined with the evaluation of a trigonometric polynomial. This filter has low complexity and can be implemented using CORDIC rotations. The second is the interpolation of nonuniform Fourier summations, where the proposed method eliminates the need to interpolate any kernel sample. Finally, the third can be viewed as a generalization of the FFT convolution algorithm and makes it possible to interpolate the output of an finite-impulse-response (FIR) filter efficiently.

A study on collaborative operation method for a new energy type dispersed power supply system by AC-EMAP model

Application of a dispersed power supply system consisting of a large-scale photovoltaic system (PV), a fuel cell (FC), and an electric double layer capacitor (EDLC) is studied in this paper. This system is operated in autonomous mode, taking account of time delay characteristics of FC. The modified Euler type Moving Average Prediction (EMAP) model is improved using short-time fast Fourier transform (ST-FFT). The Adaptive Control type EMAP (AC-EMAP) model is introduced to reduce the capacity of EDLC. This system can meet the multi-quality electric power requirements of customers, and improve voltage stability and uninterruptible power supply (UPS) function as well. Moreover, the required capacity of EDLC to compensate the fluctuation of both PV output and Load demand is clarified by a simulation based on collaborative operation method by a prediction model using software MATLAB/Simulink. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 156(1): 13–24, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20262

適応制御型EMAPモデルによる新エネルギー型分散電源の連系運用手法に関する考察

Application of a dispersed power supply system consisting of a large-scale photovoltaic system (PV), a fuel cell (FC), and an electric double layer capacitor (EDLC) is studied in this paper. This system is operated in autonomous mode, taking account of time delay characteristics of FC. The modified Euler type Moving Average Prediction (EMAP) model is improved using short-time fast Fourier transform (ST-FFT). The Adaptive Control type EMAP (AC-EMAP) model is introduced to reduce the capacity of EDLC. This system can meet the multi-quality electric power requirements of customers, and improve voltage stability and uninterruptible power supply (UPS) function as well. Moreover, the required capacity of EDLC to compensate the fluctuation of both PV output and Load demand is clarified by a simulation based on collaborative operation method by a prediction model using software MATLAB/Simulink. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 156(1): 13–24, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20262