Use Of Time-frequency Research Articles

Frequency Fluctuations in Marine Microgrids: Origins and Identification Tools

Fluctuation of frequency IS arguably the most characteristic feature of marine microgrids. T his concerns both frequency drift during step load change and all quasi-periodic fluctuation due to the impact of various factors like generators' prime movers, the governor's characteristic, and sea conditions. This article aims to provide insight into the latter phenomena. Such quasi-periodic frequency fluctuations can have detrimental effects on electrical receivers' work, especially electric motors. For the study of these fluctuations, the use of time-frequency (TF) analysis methods is the most natural choice. Among TF analysis methods, the Hilbert?Huang transform (HHT) offers some advantages to analyze signals with multiple frequency components. It is well suited for nonlinear and nonstationary signals as it estimates the instantaneous frequency of the signal, which is defined locally as the time derivative of the signal phase. The zoom discrete Fourier transform (zoom-DFT) is another method that can provide an instantaneous frequency representation of the signal using spectral zoom operation [usually a chirp z transform (CZT) method is used] and with that can portray quasi-periodic frequency fluctuations, should they exist

Ventricular Fibrillation and Tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning

Background and objectiveTo safely select the proper therapy for Ventricullar Fibrillation (VF) is essential to distinct it correctly from Ventricular Tachycardia (VT) and other rhythms. Provided that the required therapy would not be the same, an erroneous detection might lead to serious injuries to the patient or even cause Ventricular Fibrillation (VF). The main novelty of this paper is the use of time-frequency (t-f) representation images as the direct input to the classifier. We hypothesize that this method allow to improve classification results as it allows to eliminate the typical feature selection and extraction stage, and its corresponding loss of information. MethodsThe standard AHA and MIT-BIH databases were used for evaluation and comparison with other authors. Previous to t-f Pseudo Wigner-Ville (PWV) calculation, only a basic preprocessing for denoising and signal alignment is necessary. In order to check the validity of the method independently of the classifier, four different classifiers are used: Logistic Regression with L2 Regularization (L2 RLR), Adaptive Neural Network Classifier (ANNC), Support Vector Machine (SSVM), and Bagging classifier (BAGG). ResultsThe main classification results for VF detection (including flutter episodes) are 95.56% sensitivity and 98.8% specificity, 88.80% sensitivity and 99.5% specificity for ventricular tachycardia (VT), 98.98% sensitivity and 97.7% specificity for normal sinus, and 96.87% sensitivity and 99.55% specificity for other rhythms. ConclusionResults shows that using t-f data representations to feed classifiers provide superior performance values than the feature selection strategies used in previous works. It opens the door to be used in any other detection applications.

Emotion Classification from EEG Signals Using Time-Frequency-DWT Features and ANN

This paper proposes the use of time-frequency and wavelet transform features for emotion recognition via EEG signals. The proposed experiment has been carefully designed with EEG electrodes placed at FP1 and FP2 and using images provided by the Affective Picture System (IAP), which was developed by the University of Florida. A total of two time-domain features, two frequen-cy-domain features, as well as discrete wavelet transform coefficients have been studied using Artificial Neural Network (ANN) as the classifier, and the best combination of these features has been determined. Using the data collected, the best detection accuracy achievable by the proposed schemed is about 81.8%.

Estimation of Combustion Engine Rotation Speed Based on Vibration Signal Analysis

Diagnostics of mechanisms and systems allows estimating their current state and taking measures if necessary to carry out preventive or other actions for elimination of possible malfunctions. Diagnostics of mechanical parts of combustion engines, as well as other mechanical devices, mechanisms and systems as a whole, is the relevant objective. The purpose of the paper is to research the possible use of time-frequency autocorrelated function to analyze the combustion engine vibration signals to determine possible defects in its work. The paper introduces the basic possible definition of engine operation processes characteristics, being accompanied by emergence of additional frequency components in a spectrum of engine vibrosignal. The time-frequency correlation analysis allows receiving out of spectrum of engine vibrosignal additional information on weak periodic signals characterizing existence of any malfunctions of its mechanical part.

Time-frequency vibration analysis for the detection of motor damages caused by bearing currents

Motor failure due to bearing currents is an issue that has drawn an increasing industrial interest over recent years. Bearing currents usually appear in motors operated by variable frequency drives (VFD); these drives may lead to common voltage modes which cause currents induced in the motor shaft that are discharged through the bearings. The presence of these currents may lead to the motor bearing failure only few months after system startup. Vibration monitoring is one of the most common ways for detecting bearing damages caused by circulating currents; the evaluation of the amplitudes of well-known characteristic components in the vibration Fourier spectrum that are associated with race, ball or cage defects enables to evaluate the bearing condition and, hence, to identify an eventual damage due to bearing currents. However, the inherent constraints of the Fourier transform may complicate the detection of the progressive bearing degradation; for instance, in some cases, other frequency components may mask or be confused with bearing defect-related while, in other cases, the analysis may not be suitable due to the eventual non-stationary nature of the captured vibration signals. Moreover, the fact that this analysis implies to lose the time-dimension limits the amount of information obtained from this technique. This work proposes the use of time-frequency (T-F) transforms to analyse vibration data in motors affected by bearing currents. The experimental results obtained in real machines show that the vibration analysis via T-F tools may provide significant advantages for the detection of bearing current damages; among other, these techniques enable to visualise the progressive degradation of the bearing while providing an effective discrimination versus other components that are not related with the fault. Moreover, their application is valid regardless of the operation regime of the machine. Both factors confirm the robustness and reliability of these tools that may be an interesting alternative for detecting this type of failure in induction motors.

Newborn EEG Seizure Detection Using Time-Frequency Matched Filtering

Abstract Background: The analysis of Electroencephalography (EEG) signals acquired from epileptic babies shows that seizures can be modeled as piecewise linear frequency modulated (LFM) signals. This fact motivated the use of time-frequency matched filters (TFMFs) for seizure detection in newborn EEG. A TFMF is characterized by a unique test statistic, which is found based on the time-frequency (TF) correlation between the signal under analysis and a template. The test statistic is compared with a threshold to determine the presence or absence of the template in the signal under analysis. Objectives: We present two seizure detection algorithms based on the general class of TFMFs and an improved algorithm in the ambiguity domain and evaluate their performance using real EEG signals. Methods: The method includes the following stages: Based on TF analysis of newborn EEG, we create a template set containing M piecewise LFM signals with L pieces and slopes. We define test statistics based on the TF correlation...

Time-Frequency Masking for Speech Separation and Its Potential for Hearing Aid Design

A new approach to the separation of speech from speech-in-noise mixtures is the use of time-frequency (T-F) masking. Originated in the field of computational auditory scene analysis, T-F masking performs separation in the time-frequency domain. This article introduces the T-F masking concept and reviews T-F masking algorithms that separate target speech from either monaural or binaural mixtures, as well as microphone-array recordings. The review emphasizes techniques that are promising for hearing aid design. This article also surveys recent studies that evaluate the perceptual effects of T-F masking techniques, particularly their effectiveness in improving human speech recognition in noise. An assessment is made of the potential benefits of T-F masking methods for the hearing impaired in light of the processing constraints of hearing aids. Finally, several issues pertinent to T-F masking are discussed.

Comparison of the Performances of Parametric Estimation, Time-Frequency, Wavelet and Segmentation with Detection of Abrupt Changes in Non-Destructive Evaluation

Laboratory studies of absorption frequency behavior in rocks often use spectral ratios of digitally recorded ultrasonic signals which have been transmitted through a rock sample and a reference sample of a very low absorption, respectively. It is proposed to treat the digitally recorded signals as autoregressive AR process which, using recursive adaptive filter concepts. The purpose of this paper is to show the advantages of the parametric methods over Fourier analysis method for characterization of materials, and by use of time frequency, time-scale, and abrupt changes detection (Linear Predictive Error algorithm (LPE)) the choice of the signal length in spectral ratio method is solved by segmentation. Also, as a tool of analysis time-frequency and time-scale are very adapted for faults discrimination.

Mechanical signature analysis using time-frequency signal processing: application to internal combustion engine knock detection

Signature analysis consists of the extraction of information from measured signal patterns. The work presented in this paper illustrates the use of time-frequency (TF) analysis methods for the purpose of mechanical signature analysis. Mechanical signature analysis is a mature and developed field; however, TF analysis methods are relatively new to the field of mechanical signal processing, having mostly been developed in the present decade, and have not yet been applied to their full potential in this field of engineering applications. Some of the ongoing efforts are briefly reviewed in this paper. One important application of TF mechanical signature analysis is the diagnosis of faults in mechanical systems. In this paper we illustrate how the use of joint TF signal representations can result in tangible benefits when analyzing signatures generated by transient phenomena in mechanical systems, such as might be caused by faults or otherwise abnormal operation. This paper also explores signal detection concepts in the joint TF domain and presents their application to the detection of internal combustion engine knock.