Specific requirements are designed and implemented in electronic and telecommunication systems for received signals, especially high-frequency ones, to examine and control the signal radiation. However, as a serious drawback, no special requirements are considered for the transmitted signals from a subsystem. Different industries have always been struggling with electromagnetic interferences affecting their electronic and telecommunication systems and imposing significant costs. It is thus necessary to specifically investigate this problem as every device is continuously exposed to interferences. Signal processing allows for the decomposition of a signal to its different components to simulate each component. Radiation control has its specific complexities in systems, requiring necessary measures from the very beginning of the design. This study attempted to determine the highest radiation from a subsystem by estimating the radiation fields. The study goal was to investigate the level of radiations received and transmitted from the adjacent systems, respectively, and present methods for control and eliminate the existing radiations. The proposed approach employs an algorithm which is based on multi-component signals, defect, and the radiation shield used in the subsystem. The algorithm flowchart focuses on the separation and of signal components and electromagnetic interference reduction. In this algorithm, the detection process is carried out at the bounds of each component, after which the separation process is performed in the vicinity of the different bounds. The proposed method works based on the Fourier transform of impulse functions for signal components decomposition that was employed to develop an algorithm for separation of the components of the signals input to the subsystem.
In this paper, two new sinusoidal signal frequency estimators calculated on the basis of four equally spaced signal samples are presented. These estimators are called four-point estimators. Simulation and experimental research consisting in signal frequency estimation using the invented estimators have been carried out. Simulation has also been performed for frequency tracking. The simulation research was carried out applying the MathCAD computer program that determined samples of a sinusoidal signal disturbed by Gaussian noise. In the experimental research, sinusoidal signal samples were obtained by means of a National Instruments PCI-6024E data acquisition card and an Agilent 33220A function generator. On the basis of the collected samples, the values of four-point estimators invented by the authors and, for comparison, the values of three- and four-point estimators proposed by Vizireanu were determined. Next, estimation errors of the signal frequency were determined. It has been shown that the invented estimators can estimate a signal frequency with greater accuracy.
In this paper, a discrete wavelet transform (DWT) based approach is proposed for power system frequency estimation. Unlike the existing frequency estimators mainly used for power system monitoring and control, the proposed approach is developed for fundamental frequency estimation in the field of energy metering of nonlinear loads. The characteristics of a nonlinear load is that the power signal is heavily distorted, composed of harmonics, inter-harmonics and corrupted by noise. The main idea is to predetermine a series of frequency points, and the mean value of two frequency points nearest to the power system frequency is accepted as the approximate solution. Firstly the input signal is modulated with a series of modulating signals, whose frequencies are those frequency points. Then the modulated signals are decomposed into individual frequency bands using DWT, and differences between the maximum and minimum wavelet coefficients in the lowest frequency band are calculated. Similarities among power system frequency and those frequency points are judged by the differences. Simulation results have proven high immunity to noise, harmonic and inter-harmonic interferences. The proposed method is applicable for real-time power system frequency estimation for electric energy measurement of nonlinear loads.
This paper presents a simple DFT-based golden section searching algorithm (DGSSA) for the single tone frequency estimation. Because of truncation and discreteness in signal samples, Fast Fourier Transform (FFT) and Discrete Fourier Transform (DFT) are inevitable to cause the spectrum leakage and fence effect which lead to a low estimation accuracy. This method can improve the estimation accuracy under conditions of a low signal-to-noise ratio (SNR) and a low resolution. This method firstly uses three FFT samples to determine the frequency searching scope, then – besides the frequency – the estimated values of amplitude, phase and dc component are obtained by minimizing the least square (LS) fitting error of three-parameter sine fitting. By setting reasonable stop conditions or the number of iterations, the accurate frequency estimation can be realized. The accuracy of this method, when applied to observed single-tone sinusoid samples corrupted by white Gaussian noise, is investigated by different methods with respect to the unbiased Cramer-Rao Low Bound (CRLB). The simulation results show that the root mean square error (RMSE) of the frequency estimation curve is consistent with the tendency of CRLB as SNR increases, even in the case of a small number of samples. The average RMSE of the frequency estimation is less than 1.5 times the CRLB with SNR = 20 dB and N = 512.
Based on real-time multi-domain communication signal analysis architecture, a high-efficiency blind carrier frequency estimation algorithm using the power spectrum symmetry of the measured modulated signal is presented. The proposed algorithm, which utilizes the moving averaged power spectrum achieved by the realtime spectrum analysis, iteratively identifies the carrier frequency in according to the power difference between the upper sideband and lower sideband, which is defined and revised by the estimated carrier frequency in each iteration. When the power difference of the two sidebands converges to the preset threshold, the carrier frequency can be obtained. For the modulation analysis, the measured signal can be coarsely compensated by the estimated result, and the residual carrier frequency error is eliminated by a following carrier synchronization loop. Compared with previous works, owing to the moving averaged power spectrum normalization and the smart iterative step variation mechanism for the two sidebands definition, the carrier frequency estimation accuracy and speed can be significantly improved without increasing the computational effort. Experimental results are included to demonstrate the outstanding performance of the proposed algorithm.
Focus of the vibration expert community shifts more and more towards diagnosing machines subjected to varying rotational speeds and loads. Such machines require order analysis for proper fault detection and identification. In many cases phase markers (tachometers, encoders, etc) are used to help performing the resampling of the vibration signals to remove the speed fluctuations and smearing from the spectrum (order tracking). However, not all machines have the facility to install speed tracking sensors, due to design or cost reasons, and the signal itself has to then be used to extract this information. This paper is focused on the problem of speed tracking in wind turbines, which represent typical situations for speed and load variation. The basic design of a wind turbine is presented. Two main types of speed control i.e. stall and pitch control are presented,. The authors have investigated two methods of speed tracking, using information from the signal (without relying on a speed signal). One method is based on extracting a reference signal to use as a tachometer, while the other is phase-based (phase demodulation). Both methods are presented and applied to the vibration data from real wind turbines. The results are compared with each other and with the actual speed data.
Condition monitoring of machines working under non-stationary operations is one of the most challenging problems in maintenance. A wind turbine is an example of such class of machines. One of effective approaches may be to identify operating conditions and investigate their influence on used diagnostic features. Commonly used methods based on measurement of electric current, rotational speed, power and other process variables require additional equipment (sensors, acquisition cards) and software. It is proposed to use advanced signal processing techniques for instantaneous shaft speed recovery from a vibration signal. It may be used instead of extra channels or in parallel as signal verification.
This overview paper presents and compares different methods traditionally used for estimating damped sinusoid parameters. Firstly, direct nonlinear least squares fitting the signal model in the time and frequency domains are described. Next, possible applications of the Hilbert transform for signal demodulation are presented. Then, a wide range of autoregressive modelling methods, valid for damped sinusoids, are discussed, in which frequency and damping are estimated from calculated signal linear self-prediction coefficients. These methods aim at solving, directly or using least squares, a matrix linear equation in which signal or its autocorrelation function samples are used. The Prony, Steiglitz-McBride, Kumaresan-Tufts, Total Least Squares, Matrix Pencil, Yule-Walker and Pisarenko methods are taken into account. Finally, the interpolated discrete Fourier transform is presented with examples of Bertocco, Yoshida, and Agrež algorithms. The Matlab codes of all the discussed methods are given. The second part of the paper presents simulation results, compared with the Cramér-Rao lower bound and commented. All tested methods are compared with respect to their accuracy (systematic errors), noise robustness, required signal length, and computational complexity.
This paper proposed a new OFDM scheme called damped zero-pseudorandom noise orthogonal frequency division multiplexing (DZPN-OFDM) scheme. In the proposed scheme, ZPN-OFDM non-zero part is damped to reduce its energy, thus the mutual interference power in-between the data and training blocks with conservative the pseudo-noise conventional properties required for channel estimation or synchronization. The motivation of this paper is the OFDM long guard interval working in wide dispersion channels, whereas a significant energy is wasted when the conventional ZPN-OFDM is used as well as the BER performance is also degraded. Moreover, the proposed scheme doesn’t duplicate the guard interval to solve the ZPN-OFDM spectrum efficiency loss problem. Both detailed performance analysis and simulation results show that the proposed DZPNOFDM scheme can, indeed, offer significant bit error rate, spectrum efficiency and energy efficiency improvement.
The paper deals with frequency estimation methods of sine-wave signals for a few signal cycles and consists of two parts. The first part contains a short overview where analytical error formulae for a signal distorted by noise and harmonics are presented. These formulae are compared with other accurate equations presented previously by the authors which are even more accurate below one cycle in the measurement window. The second part contains a comparison of eight estimation methods (ESPRIT, TLS, Prony LS, a newly developed IpDFT method and four other 3-point IpDFT methods) in respect of calculation time and accuracy for an ideal sine-wave signal, signal distorted by AWGN noise and a signal distorted by harmonics. The number of signal cycles is limited from 0.1 to 3 or 5. The results enable to select the most accurate/ fastest estimation method in various measurement conditions. Parametric methods are more accurate but also much slower than IpDFT methods (up to 3000 times for the number of samples equal to 5000). The presented method is more accurate than other IpDFT methods and much faster than parametric methods, which makes it possible to use it as an alternative, especially in real-time applications.
Fast and accurate grid signal frequency estimation is a very important issue in the control of renewable energy systems. Important factors that influence the estimation accuracy include the A/D converter parameters in the inverter control system. This paper presents the influence of the number of A/D converter bits b, the phase shift of the grid signal relative to the time window, the width of the time window relative to the grid signal period (expressed as a cycle in range (CiR) parameter) and the number of N samples obtained in this window with the A/D converter on the developed estimation method results. An increase in the number b by 8 decreases the estimation error by approximately 256 times. The largest estimation error occurs when the signal module maximum is in the time window center (for small values of CiR) or when the signal value is zero in the time window center (for large values of CiR). In practical applications, the dominant component of the frequency estimation error is the error caused by the quantization noise, and its range is from approximately 8×10-10 to 6×10-4.
The soft tissue attenuation is an interesting parameter from medical point of view, because the value of attenuation coefficient is often related to the state of the tissue. Thus, the imaging of the attenuation coefficient distribution within the tissue could be a useful tool for ultrasonic medical diagnosis. The method of attenuation estimation based on tracking of the mean frequency changes in a backscattered signal is presented in this paper. The attenuation estimates are characterized by high variance due to stochastic character of the backscattered ultrasonic signal and some special methods must be added to data processing to improve the resulting images. The following paper presents the application of Spatial Compounding (SC), Frequency Compounding (FC) and the combination of both. The resulting parametric images are compared by means of root-mean-square errors. The results show that combined SC and FC techniques significantly improve the quality and accuracy of parametric images of attenuation distribution.
Estimating the fundamental frequency and harmonic parameters is basic for signal modelling in a power supply system. Differing from the existing parameter estimation algorithms either in power quality monitoring or in harmonic compensation, the proposed algorithm enables a simultaneous estimation of the fundamental frequency, the amplitudes and phases of harmonic waves. A pure sinusoid is obtained from an input multiharmonic input signal by finite-impulse-response (FIR) comb filters. Proposed algorithm is based on the use of partial derivatives of the processed signal and the weighted estimation procedure to estimate the fundamental frequency, the amplitude and the phase of a multi-sinusoidal signal. The proposed algorithm can be applied in signal reconstruction, spectral estimation, system identification, as well as in other important signal processing problems. The simulation results verify the effectiveness of the proposed algorithm.