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.
This paper presents a new modification of the least-squares Prony’s method with reduced sampling, which allows for a significant reduction in the number of the analysed signal samples collected per unit time. The specific combination of non-uniform sampling with Prony’s method enables sampling of the analysed signals at virtually any average frequency, regardless of the Nyquist frequency, maintaining high accuracy in parameter estimation of sinusoidal signal components. This property allows using the method in measuring devices, such as for electric power quality testing equipped with low power signal processors, which in turn contributes to reducing complexity of these devices. This paper presents research on a method for selecting a sampling frequency and an analysis window length for the presented method, which provide maximum estimation accuracy for Prony’s model component parameters. This paper presents simulation tests performed in terms of the proposed method application for analysis of harmonics and interharmonics in electric power signals. Furthermore, the paper provides sensitivity analysis of the method, in terms of common interferences occurring in the actual measurement systems.
In this paper, the author presents the possibility of using phase trajectory for detecting damage in an axial piston pump. The wear on main part of pump elements, such as the rotor and the valve plate, was investigated, and phase trajectories were determined based on vibration signal measured in three directions on the pump's body. In order to obtain a quantitative measure of the analyzed trajectory, the At_{p,i} parameter was introduced, and the relation between this parameter and the wear on the pump's parts was determined.
This study presents a possibility of detecting wear of a valve plate in multi-piston axial pump based on time-frequency analysis of measured signals. Short-time Fourier transform STFT and the generalized Wigner-Ville algorithm WVD were used for this purpose. The tests were carried out on a multi-piston axial pump with swinging plate, in which the worn valve plates were mounted. Valve plate wear was related with the formation of flow micro-channels between the pump suction hole and its pumping hole on the plate transition zone surface. The developed channels initiate flow of the operational fluid, the results of which is lack of leak-tightness between suction and pumping zones, associated with a decrease in operational pressure and drop in general efficiency.
Quantitative ultrasound has been widely used for tissue characterization. In this paper we propose a new approach for tissue compression assessment. The proposed method employs the relation between the tissue scatterers’ local spatial distribution and the resulting frequency power spectrum of the backscattered ultrasonic signal. We show that due to spatial distribution of the scatterers, the power spectrum exhibits characteristic variations. These variations can be extracted using the empirical mode decomposition and analyzed. Validation of our approach is performed by simulations and in-vitro experiments using a tissue sample under compression. The scatterers in the compressed tissue sample approach each other and consequently, the power spectrum of the backscattered signal is modified. We present how to assess this phenomenon with our method. The proposed in this paper approach is general and may provide useful information on tissue scattering properties.
A computer measurement system, designed and built by authors, dedicated to location and description of partial discharges (PD) in oil power transformers examined by means of the acoustic emission (AE) method is presented. The measurement system is equipped with 8 measurement channels and ensures: monitoring of signals, registration of data in real time within a band of 25–1000 kHz in laboratory and real conditions, basic and advanced analysis of recorded signals. The basic analysis carried out in the time, frequency and time-frequency domains deals with general properties of the AE signals coming from PDs. The advanced analysis, performed in the discrimination threshold domain, results in identification of signals coming from different acoustic sources as well as location of these sources in the examined transformers in terms of defined by authors descriptors and maps of these descriptors on the side walls of the tested transformer tank. Examples of typical results of laboratory tests carried out with the use of the built-in measurement system are presented.
In order to understand commands given through voice by an operator, user or any human, a robot needs to focus on a single source, to acquire a clear speech sample and to recognize it. A two-step approach to the deconvolution of speech and sound mixtures in the time-domain is proposed. At first, we apply a deconvolution procedure, constrained in the sense, that the de-mixing matrix has fixed diagonal values without non-zero delay parameters. We derive an adaptive rule for the modification of the de-convolution matrix. Hence, the individual outputs extracted in the first step are eventually still self-convolved. This corruption we try to eliminate by a de-correlation process independently for every individual output channel.
The possibility of distinguishing and assessing the influences of defects in particular pump elements by registering vibration signals at characteristic points of the pump body would be a valuable way for obtaining diagnostic information. An effective tool facilitating this task could be a well designed and identified dynamic model of the pump. When applied for a specific type of the pump, such model could additionally help to improve its construction. This paper presents model of axial piston positive displacement pump worked out by the authors. After taking the simplifying assumptions and dividing the pump into three sets of elements, it was possible to build a discrete dynamic model with 13 degrees of freedom. According to the authors' intention, the developed dynamic model of the multi-piston pump should be used for damage simulation in its individual elements. By gradual change in values of selected construction parameters of the object (for example: stiffness coefficients, damping coefficients), it is possible to perform simulation of wear in the pump. Initial verification of performance of the created model was done to examine the effect of abrasive wear on the swash plate surface. The phase trajectory runs estimated at characteristics points of the pump body were used as a useful tool to determine wear of pump elements.
Detonation of explosives creates strong para-seismic vibrations. Such vibrations can damage buildings or other infrastructure located in the vicinity of such detonations, and can be burdensome to people living in such areas. This paper describes the usefulness of Matching Pursuit (MP) algorithm in assessing the impact of blasting on the surrounding areas, and proves that by taking into account frequency changes over time, vibration analysis can help make much more profound and reliable predictions in this field.
The human voice is one of the basic means of communication, thanks to which one also can easily convey the emotional state. This paper presents experiments on emotion recognition in human speech based on the fundamental frequency. AGH Emotional Speech Corpus was used. This database consists of audio samples of seven emotions acted by 12 different speakers (6 female and 6 male). We explored phrases of all the emotions – all together and in various combinations. Fast Fourier Transformation and magnitude spectrum analysis were applied to extract the fundamental tone out of the speech audio samples. After extraction of several statistical features of the fundamental frequency, we studied if they carry information on the emotional state of the speaker applying different AI methods. Analysis of the outcome data was conducted with classifiers: K-Nearest Neighbours with local induction, Random Forest, Bagging, JRip, and Random Subspace Method from algorithms collection for data mining WEKA. The results prove that the fundamental frequency is a prospective choice for further experiments.
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