Short-Time Fourier Transform Spectrogram Research Articles

INDUCED DRAFT FAN DOMINANT FREQUENCY DETECTION USING SHORT-TIME FOURIER TRANSFORM METHOD

Weak suction and large vibrations indicate an Induced Draft Fan (IDF) problem. The Fast Fourier Transform (FFT) method cannot be applied to non-stationary vibration signals. Therefore, this study aims to analyze non-stationary vibration signals for IDF vibration signals at start-up so that the source of damage to the IDF can be found. The research process begins with a brief measurement of both bearing locations with horizontal and axial axes. Processing of the vibration signal from the measurement using the FFT method and the Short Time Fourier Transform (STFT). Based on the STFT spectrogram graph for measurements on the horizontal and axial axes, the dominant frequency values are the same. The frequency with the largest amplitude value is at one RPM IDF or 25 Hz. High vibration at 1 RPM is a big indication that the IDF is experiencing unbalance.

EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN

In this work, a multi-task convolutional neural network with multi-input (MIMT-CNN) is proposed for electromagnetic interference (EMI) signals recognition and electromagnetic environment risk evaluation of the data link of unmanned aerial vehicle (UAV). The visualized performance parameters, short-time Fourier transform (STFT) spectrograms, and constellation diagrams are obtained by experiment on the electromagnetic susceptibility of UAV’s datalink. In particular, the constellation diagram is further enhanced by calculating the density distribution of sampling points to obtain the normalized density constellation. Taking the above different categories of images as the input of the expected model, the multi-element and high correlation EMI features are extracted and fused in the MIMT-CNN. Besides, the structure of series-parallel connection is adopted in the trained model and the Bayesian optimization is also used to select hyperparameters. In this case, the perception model with higher reliability can be obtained. On this basis, the performance and complexity of the obtained model with different input channels are compared. The results show that with the input of constellation diagram, especially the normalized density constellation, can significantly improve the accuracy of the model. Besides the normalized density constellation, the model with visualized performance parameters and STFT spectrogram as inputs has a much better performance.

Application of Short-time Fourier Transform in Feeder Fault Detection of Flexible Multi-state Switch

Flexible multi-state switch (FMSS) is widely used in active distribution networks. However, the medium voltage distribution network that includes FMSS requires more accurate fault detection method. To solve this problem, the application of short-time Fourier transform (STFT) in the design of FMSS fault detection methods is studied. The data of power spectrum density in the STFT spectrogram is used to define the Fault Index, Λ. The A can be used to accurately detect the fault type of FMSS in a relatively short time. In order to verify the effectiveness of this fault detection method, this paper builds a control structure for each port based on the FMSS topology and mathematical model. The FMSS model is simulated in MATLAB/Simulink, and the fault signal is analysed by STFT in MATLAB. The results show that the proposed fault detection method can accurately detect the fault type. The STFT parameters have a great influence on the fault detection result, which is studied in the parameters influence analysis.

Development of internal sound sensor using stethoscope and its applications for machine monitoring

With the development of Internet of Things (IoT), predictive maintenance (PdM) for smart manufacturing receives attentions recently. For the monitoring of machines with rotary components, sound and vibration emitted from the machines have been utilized as the meaningful information. However, sound sensors are susceptible to external noise and the costs for signal conditioning should be considered. In this paper, a stethoscope is utilized as an internal sound sensor which are capable of noise reduction and affordable as IoT solutions. After the stethoscope was assembled with a microphone, a frequency range for measurement was identified as 0~255Hz using the swept-sine technique. Two stethoscope sensors were attached to an industrial robot, and the capability of noise reduction is verified by similarity of Short-Time Fourier Transform (STFT) spectrograms from sounds with and without existence of factory noise. Next, impact tests were performed to detect collision when the robot was stationary, and collisions by human hand during robot operation were detected from STFT spectrogram analysis. Finally, the Convolutional Neural Network (CNN) model from the spectrograms was established to estimate a rotating joint axis during operation, showing 91% and 90.83% accuracy for each stethoscope, respectively.

STFT-like time frequency representations of nonstationary signal with arbitrary sampling schemes

Spectrograms provide an effective way of time–frequency representation (TFR). Among these, short-time Fourier transform (STFT) based spectrograms are widely used for various applications. However, STFT spectrogram and its revised versions suffer from two main issues: (1) there is a trade-off between time resolution and frequency resolution and (2) almost all the existing TFR methods, including STFT spectrogram, are not designed to handle arbitrary nonuniformly sampled data. To address these two issues, short-time iterative adaptive approach (ST-IAA) was recently proposed as a data-dependent adaptive spectral estimation method that can provide much enhanced TFR performance. In this paper, inspired by the ST-IAA method, we present an alternative approach, namely short-time sparse learning via iterative minimization (ST-SLIM), which can provide sparser and slightly better TFR performance than its ST-IAA counterpart. Moreover, in order to extend the applicability of ST-IAA to signals in the missing data case, we also propose a short-time missing-data iterative adaptive approach (ST-MIAA) which can retrieve the missing data effectively and outperform ST-IAA and ST-SLIM in the missing data case. We will demonstrate via simulation results the superiority of our proposed algorithms in terms of resolution, sidelobe suppression and applicability to signals with arbitrary sampling patterns.

Signals features extraction in liquid-gas flow measurements using gamma densitometry. Part 2: frequency domain

Knowledge of the structure of a flow is really significant for the proper conduct a number of industrial processes. In this case a description of a two-phase flow regimes is possible by use of the time-series analysis e.g. in frequency domain. In this article the classical spectral analysis based on Fourier Transform (FT) and Short-Time Fourier Transform (STFT) were applied for analysis of signals obtained for water-air flow using gamma ray absorption. The presented method was illustrated by use data collected in experiments carried out on the laboratory hydraulic installation with a horizontal pipe of 4.5 m length and inner diameter of 30 mm equipped with two 241Am radioactive sources and scintillation probes with NaI(Tl) crystals. Stochastic signals obtained from detectors for plug, bubble, and transitional plug – bubble flows were considered in this work. The recorded raw signals were analyzed and several features in the frequency domain were extracted using autospectral density function (ADF), cross-spectral density function (CSDF), and the STFT spectrogram. In result of a detail analysis it was found that the most promising to recognize of the flow structure are: maximum value of the CSDF magnitude, sum of the CSDF magnitudes in the selected frequency range, and the maximum value of the sum of selected amplitudes of STFT spectrogram.

Signals features extraction in liquid-gas flow measurements using gamma densitometry. Part 1: time domain

Knowledge of the structure of a flow is really significant for the proper conduct a number of industrial processes. In this case a description of a two-phase flow regimes is possible by use of the time-series analysis e.g. in frequency domain. In this article the classical spectral analysis based on Fourier Transform (FT) and Short-Time Fourier Transform (STFT) were applied for analysis of signals obtained for water-air flow using gamma ray absorption. The presented method was illustrated by use data collected in experiments carried out on the laboratory hydraulic installation with a horizontal pipe of 4.5 m length and inner diameter of 30 mm equipped with two 241Am radioactive sources and scintillation probes with NaI(Tl) crystals. Stochastic signals obtained from detectors for plug, bubble, and transitional plug – bubble flows were considered in this work. The recorded raw signals were analyzed and several features in the frequency domain were extracted using autospectral density function (ADF), cross-spectral density function (CSDF), and the STFT spectrogram. In result of a detail analysis it was found that the most promising to recognize of the flow structure are: maximum value of the CSDF magnitude, sum of the CSDF magnitudes in the selected frequency range, and the maximum value of the sum of selected amplitudes of STFT spectrogram.

Evaluating ultrasound signals of carbon steel fatigue testing using signal analysis approaches

The application of ultrasound techniques to monitor the condition of structures is becoming more prominent because these techniques can detect the early symptoms of defects such as cracks and other defects. The early detection of defects is of vital importance to avoid major failures with catastrophic consequences. An assessment of an ultrasound technique was used to investigate fatigue damage behaviour. Fatigue tests were performed according to the ASTM E466-96 standard with the attachment of an ultrasound sensor to the test specimen. AISI 1045 carbon steel was used due to its wide application in the automotive industry. A fatigue test was performed under constant loading stress at a sampling frequency of 8 Hz. Two sets of data acquisition systems were used to collect the fatigue strain signals and ultrasound signals. All of the signals were edited and analysed using a signal processing approach. Two methods were used to evaluate the signals, the integrated Kurtosis-based algorithm for z-filter technique (I-kaz) and the short-time Fourier transform (STFT). The fatigue damage behaviour was observed from the initial stage until the last stage of the fatigue test. The results of the I-kaz coefficient and the STFT spectrum were used to explain and describe the behaviour of the fatigue damage. I-kaz coefficients were ranged from 60 to 61 for strain signals and ranged from 5 to 76 for ultrasound signals. I-kaz values tend to be high at failure point due to high amplitude of respective signals. STFT spectrogram displays the colour intensity which represents the damage severity of the strain signals. I-kaz technique is found very useful and capable in assessing both stationary and non-stationary signals while STFT technique is suitable only for non-stationary signals by displaying its spectrogram.

Seismic spectral decomposition using deconvolutive short-time Fourier transform spectrogram

The spectral decomposition technique plays an important role in reservoir characterization, for which the time-frequency distribution method is essential. The deconvolutive short-time Fourier transform (DSTFT) method achieves a superior time-frequency resolution by applying a 2D deconvolution operation on the short-time Fourier transform (STFT) spectrogram. For seismic spectral decomposition, to reduce the computation burden caused by the 2D deconvolution operation in the DSTFT, the 2D STFT spectrogram is cropped into a smaller area, which includes the positive frequencies fallen in the seismic signal bandwidth only. In general, because the low-frequency components of a seismic signal are dominant, the removal of the negative frequencies may introduce a sharp edge at the zero frequency, which would produce artifacts in the DSTFT spectrogram. To avoid this problem, we used the analytic signal, which is obtained by applying the Hilbert transform on the original real seismic signal, to calculate the STFT spectrogram in our method. Synthetic and real seismic data examples were evaluated to demonstrate the performance of the proposed method.

On an efficient modification of singular value decomposition using independent component analysis for improved MRS denoising and quantification

An efficient modification of singular value decomposition (SVD) is proposed in this paper aiming at denoising and more importantly at quantifying more accurately the statistically independent spectra of metabolite sources in magnetic resonance spectroscopy (MRS). Although SVD is known in MRS applications and several efficient algorithms exist for estimating SVD summation terms in which the raw MRS data are analyzed, however, it would be more beneficial for such an analysis if techniques with the ability to estimate statistically independent spectra could be employed. SVD is known to separate signal and noise subspaces but it assumes orthogonal properties for the components comprising signal subspace, which is not always the case, and might impose heavy constraints for the MRS case. A much more relaxing constraint would be to assume statistically independent components. Therefore, a modification of the main methodology incorporating techniques for calculating the assumed statistically independent spectra is proposed by applying SVD on the MRS spectrogram through application of the short time Fourier transform (STFT). This approach is based on combining SVD on STFT spectrogram followed by an iterative application of independent component analysis (ICA). Moreover, it is shown that the proposed methodology combined with a regression analysis would lead to improved quantification of the MRS signals. An experimental study based on synthetic MRS signals has been conducted to evaluate the herein proposed methodologies. The results obtained have been discussed and it is shown to be quite promising.

Deconvolutive Short-Time Fourier Transform Spectrogram

The short-time Fourier transform (STFT) spectrogram, which is the squared modulus of the STFT, is a smoothed version of the Wigner-Ville distribution (WVD). The STFT spectrogram is 2-D convolution of the the signal WVD and the window function WVD. In this letter, we propose a deconvolutive short-time Fourier transform (DSTFT) spectrogram method, which improves the time-frequency resolution and reduces the cross-terms simultaneously by applying a 2-D deconvolution operation on the STFT spectrogram. Compared to the STFT spectrogram, the spectrogram obtained by the proposed method shows a clear improvement in the time-frequency resolution. Computer simulations are provided to illustrate the good performance of the proposed method, compared with some traditional time-frequency representation (TFR) methods.

The Wigner distribution as a tool for time-frequency analysis of fusion plasma signals: application to broadband reflectometry data

The Wigner distribution is introduced as an appropriate tool for processing data from fusion plasma diagnostics whose signals have a time varying frequency spectrum, and is thus presented as a particularly suited form for the time-frequency analysis of transients and other non-stationary phenomena. Its effectiveness is illustrated byapplying it to the problem of electron density profile measurement through broadband microwave reflectometry in fusiondevices, its advantages being demonstrated via a detailed comparison with the well known short time Fourier transformspectrogram. In particular, the Wigner distribution is used in a novel application as a means not only to retrievefrom reflectometry data the instantaneous frequency needed for profile inversion, but also to provide an accuraterepresentation of reflectometry signals in the time-frequency plane. Further, in a careful discussion stressing itsbenefits relative to more standard approaches based on the quadrature signal, the analyticsignal is proposed as being appropriate to routinely extract from reflectometry data anunambiguously defined set of instantaneous amplitude, phase and frequency. The problemsassociated with the fact that digitized reflectometry data give rise to discrete time signals areproperly handled, and the different estimates for the instantaneous frequency obtained from the Wignerdistribution and the spectrogram by calculating their first frequency moments and mean frequencies,as well as by locating their peaks, are also compared.