Power Spectrum Estimation Method Research Articles

Compressed covariance sensing for blade tip timing measurement

Due to harsh working conditions, rotating blades are prone to failures. Thus, it is urgently needed to monitor blade health. Blade tip timing (BTT) is a promising non-contact vibration technique for rotating blades owing to its high efficiency and long service time. However, due to severe undersampling, traditional spectrum analysis methods are ineffective in identifying characteristic parameters and recovering the power spectrum of vibrations for condition monitoring. To address this issue, this paper proposes a compressed covariance sensing-based BTT (CCS-BTT) method for power spectrum estimation. Specifically, we build a compressed covariance sensing representation model for BTT signal, where the covariance representation can be non-underdetermined owing to the Hermitian Toeplitz structure of the covariance matrix. Then, a universal covariance sampling pattern (UCSP) is derived to ensure the complete covariance information can be efficiently recovered without sparsity constraint. By utilizing the inherent periodicity, the derived UCSP significantly improves the efficiency of BTT measurement. Moreover, for practicality, we present a series of optimized layouts based on UCSP that cover almost all possible cases of probe numbers in BTT measurement. Finally, extensive simulations and experiments validate the effectiveness and performance of CCS-BTT. By shifting the perspective from the signal itself to its covariance, CCS-BTT opens many avenues for the development of BTT signal processing. In addition, the proposed method is expected to be used in deterministic compressive sampling of wide-sense stationary signals.

Power spectrum estimation methods on intracluster medium surface brightness fluctuations

Accurate estimation of galaxy cluster masses is a central problem in cosmology. Turbulence is believed to introduce significant deviations from the hydrostatic mass estimates. Estimation of turbulence properties is complicated by projection of the 3D cluster onto the 2D plane of the sky, and is commonly done in the form of indirect probes from fluctuations in the X-ray surface brightness and Sunyaev-Zeldovich effect maps. In this paper, we address this problem using simulations. We examine different methods for estimating the power spectrum on 2D projected fluctuation data, emulating data projected onto a 2D plane of the sky, and comparing them to the original, expected 3D power spectrum. Noise can contaminate the power spectrum of ICM observations, so we also briefly compare a few methods of reducing noise in the images for better spectral estimation.

Application of Bispectrum Dimensionality Reduction Method in Ultrasonic Echo Signal Processing

Processing ultrasonic echo signals to obtain high-precision residual thickness information of the pipeline wall is the key to nondestructive testing of corrosion of a long-distance pipeline. The traditional power spectrum estimation method assumes that an analyzed echo signal is Gaussian, and the useful information is insufficiently extracted, which leads to errors in the processing results. In this paper, to solve this problem, the bispectrum, which requires the least amount of computation in higher-order spectral estimation, is proposed to process an echo signal with a non-minimum phase and non-Gaussian characteristics. The bispectrum is projected onto a one-dimensional frequency space using the dimensionality reduction method, and one-dimensional diagonal slices of the bispectrum are extracted to analyze the characteristics of the echo signal, which significantly improves the intuitiveness of data processing. The experimental results show that the bispectrum dimensionality reduction method has high accuracy in processing ultrasonic echo signals, and the relative error of the residua wall thickness is below 2%. A C-scan image displaying the shape, size, depth, and other characteristics of pipeline corrosion obtained by the proposed method is much better than that using the traditional power spectrum estimation method. Therefore, the proposed method is suitable for nondestructive testing of corrosion of long-distance pipelines.

The Auto-Regressive Model and Spectrum Information Entropy Judgment Method for High Intensity Focused Ultrasound Echo Signal

For accurate evaluation of high intensity focused ultrasound (HIFU) treatment effect, it is of great importance to effectively judge whether the sampled signal is the HIFU echo signal or the noise signal. In this paper, a judgment method based on an auto-regressive (AR) model and spectrum information entropy is proposed. In total, 188 groups of data are obtained while the HIFU source is on or off through experiments, and these sampled signals are judged by this method. The judgment results of this method are compared with empirical judgments. It is found that when the segment number for the power spectrum estimated by AR model is 14 to 17, the judgment results of this method have a higher consistency with empirical judgments, and Accuracy, Sensitivity and Specificity all have good values. Moreover, after comparing and analyzing this method with the classic power spectrum estimation method, it is found that the recognition rate of the two sampled signals of this method is higher than that of the classic power spectrum estimation method. Therefore, this method can effectively judge the different types of sampled signals.

Research on variation characteristics of fault activity based on satellite thermal infrared remote sensing data taking an example of Songyuan M S5.7 in 2018

In this paper, we use the brightness temperature data of the Chinese stationary meteorological satellite and wavelet transform and power spectrum estimation methods to investigate the thermal infrared anomalies of these three greater than M S4.5 earthquakes in Songyuan area around 2017. The results show that the anomalies gradually expanded along the strike of Yilan-Yitong fault and Mishan-Dunhua fault zone before the M S4.9 earthquake on July 23, 2017 and the M S 5.7 earthquake on May 28, 2018. The anomalies existed in the eastern margin of the Songliao Basin, but the epicenter of the earthquake was not in that area. The abnormality was not obvious before the M S5.1 earthquake on May 18, 2019. It is a possibility that greenhouse gases, such as CO2 and CH4, were released in the greatest amount in the basin, before and after the previous two earthquakes.

Optimal substitution rate analysis of submarine natural gas hydrates considering geological and hydrological conditions

In order to improve the ability of data mining and information scheduling for the best replacement rate of seabed natural gas hydrate under geological and hydrological conditions, an optimization algorithm for estimating the best replacement rate of seabed natural gas hydrate is proposed. By using piecewise linear estimation method, the characteristic data of submarine gas hydrate database are grouped and reconstructed, and the optimal replacement rate of submarine gas hydrate in the database is fused with related information. According to the spatial distribution of geological and hydrological conditions, the optimal replacement rate of submarine gas hydrate is adaptively dispatched. The optimal replacement rate of submarine gas hydrate is extracted by frequent item mining method, and the extracted optimal replacement rate of submarine gas hydrate is processed in turn. Finally, the unbiased estimation of the optimal replacement rate of submarine gas hydrate is realized by using regularized power spectrum estimation method. The experimental results show that the design method has small deviation and high accuracy in estimating the best substitution rate of submarine natural gas hydrate.

Multiantenna-Assisted Wideband Spectrum Sensing Based on Sub-Nyquist Sampling

We consider the problem of wideband spectrum sensing, where a multi-antenna secondary user (SU) attempts to identify the frequency locations of a number of single-antenna primary users (PUs)' transmissions that spread over a wide frequency band. To overcome the sampling rate bottleneck, we propose a new sub-Nyquist sampling architecture as well as a compressed power spectrum estimation method whose objective is to reconstruct the power spectrum of the transmitted signals from PUs. The proposed sub-Nyquist sampling scheme has a simple structure, and a power spectrum recovery analysis shows that the proposed method can achieve a significant sampling rate reduction, without imposing a sparse constraint on the frequency band under monitoring. Simulation results are presented to show the effectiveness of the proposed method.

Calculation and Analysis of Fatigue Life of Gravity Dam Based on FEM

Gravity dam is a hydraulic structure subjected to high stress loads by its own weight to maintain its stability. Gravity dams mainly bear the pressure of reservoir water changes, the impact of high-intensity earthquakes, the wind and waves, the vibration of generator sets, etc. Therefore, the structural safety of dams is a major event related to engineering safety. It has very important theoretical significance and practical value for dam structural safety analysis. In this paper, the concrete gravity dam is taken as the main research object. The dam is modeled by the finite element method. According to the water level of the dam reservoir, the static and dynamic analysis of the dam under various responses, the deformation situation and strain situation of the dam is studied. Then combined with seismic wave selection and power spectrum estimation method, the seismic performance of the dam is analyzed. The specific method is to find the weak point of the dam and study the fatigue life of the dam to obtain the overall fatigue life of the dam.

Modeling co-seismic thermal infrared brightness anomalies in petroliferous basins surrounding the North and East of the Qinghai–Tibet Plateau

Co-seismic gas leakage usually occurs on the edge of seismic faults in petroliferous basins, and it may have an impact on the local environment, such as the greenhouse effect, which can cause thermal infrared brightness anomalies. Using wavelet transform and power spectrum estimation methods, we processed brightness temperature data from the Chinese geostationary meteorological satellite FY-C/E. We report similarities between the co-seismic thermal infrared brightness (CTIB) anomalies before, during and after earthquakes that occurred at the edges of the Sichuan, Tarim, Qaidam, and Junggar basins surrounding the North and East of the Qinghai–Tibet Plateau in western China. Additionally, in each petroliferous basin, the area of a single CTIB anomaly accounted for 50% to 100% of the basin area, and the spatial distribution similarities in the CTIB anomalies existed before, during and after these earthquakes. To better interpret the similarities, we developed a basin warming effect model based on geological structures and topography. The model suggests that in a petroliferous basin with a subsurface gas reservoir, gas leakage could strengthen with the increasing stress before, during, and even after an earthquake. The accumulation of these gases, such as the greenhouse gases CH4 and CO2, results in the CTIB anomalies. In addition, we conclude that the CTIB anomalies are strengthened by the high mountains (altitude ~5000 m) around the basins and the basins’ independent climatic conditions. This work provides a new perspective from which to understand the CTIB anomalies in petroliferous basins surrounding the North and East of the Qinghai–Tibet Plateau.

Fast Compressed Power Spectrum Estimation: Toward a Practical Solution for Wideband Spectrum Sensing

There has been a growing interest in wideband spectrum sensing due to its applications in cognitive radios and electronic surveillance. To overcome the sampling rate bottleneck for wideband spectrum sensing, in this paper, we study the problem of compressed power spectrum estimation whose objective is to reconstruct the power spectrum of a wide-sense stationary signal based on sub-Nyquist samples. By exploring the sampling structure inherent in the multicoset sampling scheme, we develop a computationally efficient method for power spectrum reconstruction. An important advantage of our proposed method over existing compressed power spectrum estimation methods is that our proposed method, whose primary computational task consists of fast Fourier transform (FFT), has a very low computational complexity. Such a merit makes it possible to efficiently implement the proposed algorithm in a practical field-programmable gate array (FPGA)-based system for real-time wideband spectrum sensing. Our proposed method also provides a new perspective on the power spectrum recovery condition, which leads to a result similar to what was reported in prior works. Simulation results are presented to show the computational efficiency and the effectiveness of the proposed method.

Robust statistics towards detection of the 21 cm signal from the Epoch of Reionization

We explore methods for robust estimation of the 21 cm signal from the Epoch of Reionisation (EoR). A Kernel Density Estimator (KDE) is introduced for measuring the spatial temperature fluctuation power spectrum from the EoR. The KDE estimates the underlying probability distribution function of fluctuations as a function of spatial scale, and contains different systematic biases and errors to the typical approach to estimating the fluctuation power spectrum. Extraction of histograms of visibilities allows moments analysis to be used to discriminate foregrounds from 21 cm signal and thermal noise. We use the information available in the histograms, along with the statistical dis-similarity of foregrounds from two independent observing fields, to robustly separate foregrounds from cosmological signal, while making no assumptions about the Gaussianity of the signal. Using two independent observing fields to robustly discriminate signal from foregrounds is crucial for the analysis presented in this paper. We apply the techniques to 13 hours of Murchison Widefield Array (MWA) EoR data over two observing fields. We compare the output to that obtained with a comparative power spectrum estimation method, and demonstrate the reduced foreground contamination using this approach. Using the second moment obtained directly from the KDE distribution functions yields a factor of 2-3 improvement in power for k < 0.3hMpc^{-1} compared with a matched delay space power estimator, while weighting data by additional statistics does not offer significant improvement beyond that available for thermal noise-only weights.

Self-similarity and multifractality in human brain activity: A wavelet-based analysis of scale-free brain dynamics

BackgroundThe temporal structure of macroscopic brain activity displays both oscillatory and scale-free dynamics. While the functional relevance of neural oscillations has been largely investigated, both the nature and the role of scale-free dynamics in brain processing have been disputed. New methodHere, we offer a novel method to rigorously enrich the characterization of scale-free brain activity using a robust wavelet-based assessment of self-similarity and multifractality. For this, we analyzed human brain activity recorded with magnetoencephalography (MEG) while participants were at rest or performing a visual motion discrimination task. ResultsFirst, we report consistent infraslow (from 0.1 to 1.5 Hz) scale-free dynamics (i.e., self-similarity and multifractality) in resting-state and task data. Second, we observed a fronto-occipital gradient of self-similarity reminiscent of the known hierarchy of temporal scales from sensory to higher-order cortices; the anatomical gradient was more pronounced in task than in rest. Third, we observed a significant increase of multifractality during task as compared to rest. Additionally, the decrease in self-similarity and the increase in multifractality from rest to task were negatively correlated in regions involved in the task, suggesting a shift from structured global temporal dynamics in resting-state to locally bursty and non Gaussian scale-free structures during task. Comparison with existing method(s)We showed that the wavelet leader based multifractal approach extends power spectrum estimation methods in the way of characterizing finely scale-free brain dynamics. ConclusionsAltogether, our approach provides novel fine-grained characterizations of scale-free dynamics in human brain activity.

Wideband Spectrum Sensing Based on Single-Channel Sub-Nyquist Sampling for Cognitive Radio.

Spectrum sensing is an important task in cognitive radio. However, currently available Analog-to-Digital Converters (ADC) can hardly satisfy the sampling rate requirement for wideband signals. Even with such an ADC, the cost is extremely high in terms of price and power consumption. In this paper, we propose a spectrum-sensing method based on single-channel sub-Nyquist sampling. Firstly, a serial Multi-Coset Sampling (MCS) structure is designed to avoid mismatches among sub-ADCs in the traditional parallel MCS. Clocks of the sample/hold and ADC are provided by two non-uniform sampling clocks. The cooperation between these two non-uniform sampling clocks shifts the high sampling rate burden from the ADC to the sample/hold. Secondly, a power spectrum estimation method using sub-Nyquist samples is introduced, and an efficient spectrum-sensing algorithm is proposed. By exploiting the frequency-smoothing property, the proposed efficient spectrum-sensing algorithm only needs to estimate power spectrum at partial frequency bins to conduct spectrum sensing, which will save a large amount of computational cost. Finally, the sampling pattern design of the proposed serial MCS is given, and it is proved to be a minimal circular sparse ruler with an additional constraint. Simulations show that mismatches in traditional parallel MCS have a serious impact on spectrum-sensing performance, while the proposed serial MCS combined with the efficient spectrum-sensing algorithm exhibits outstanding spectrum-sensing performance at much lower computational cost.

Time-frequency Research of Pulse Rate Variability Based on the EEMD

In this paper, based on the feature of ensemble empirical mode decomposition(EEMD) which is very suitable for analyzing non-stationary and nonlinear signals, the pulse rate variability (PRV) signal is decomposed into multiple intrinsic mode function (IMF) components by EEMD, to calculate the energy of each IMF component respectively, and high-frequency energy(EHF), low-frequency energy(ELF) and very low-frequency energy(EVLF) of PRV were reconstructed from the energy of the IMF components according to the characteristics of each IMF component spectrum. This method is compared with the traditional AR power spectrum estimation method through the experiment. The results show that the correlation coefficient of each frequency domain parameter corresponding to PRV obtained by the two methods is higher than 0.96, which indicates that the method can truly reflect the sympathetic nerve and vagus nerve activity.

English

Communication signals plays vital role in the automatic transmission of data over various applications and also in the various logistic and electrical activities of defence services. Analysis of communication signals is essential to ensure spectral efficiency and integrity of information. The main objective of the analysis is to minimize the noise present in the communication signals like voice, telegraphy, teletype writer and facsimile (FAX) etc. Nonuniform sampling occurs in many applications where uniform sampling either not possible or practically not achievable. This case relates to event based sampling in the time domain, such as queue process in routers, data networks and astronomical data processing etc. Non-uniform sampling signal can be estimated using FFT, Several methods to compute an approximate Fourier Transform for posterior analysis in terms of alias suppression and leakage. Though most of the work was focused on the development of posterior analysis of the signal a priori estimation of stochastic properties of the signal may result in better transformation approximation. In this way the frequency resolution of noise spectrum will be minimized. For optimum level minimizing purpose of frequency resolution, non-parametric power spectrum estimation methods are considered. The main focus of this paper is minimization of frequency resolution of noise spectrum and increasing frequency resolution of signal spectrum. For this purpose different non-parametric power spectrum estimation methods are used. In the non-parametric methods, periodogram is modified averaging and smoothing using different window techniques. This leads to the minimization of frequency resolution and variance with the help of non -parametric power spectrum estimation methods.

Robust Estimation of Non-Stationary Noise Power Spectrum for Speech Enhancement

We propose a novel method for noise power spectrum estimation in speech enhancement. This method called extended-DATE (E-DATE) extends the $d$ -dimensional amplitude trimmed estimator (DATE), originally introduced for additive white gaussian noise power spectrum estimation in “Robust estimation of noise standard deviation in presence of signals with unknown distributions and occurrences” (D. Pastor and F. Socheleau, IEEE Trans. Signal Processing, vol. 60, no. 4, pp. 1545–1555, Apr. 2012) to the more challenging scenario of non-stationary noise. The key idea is that, in each frequency bin and within a sufficiently short time period, the noise instantaneous power spectrum can be considered as approximately constant and estimated as the variance of a complex gaussian noise process possibly observed in the presence of the signal of interest. The proposed method relies on the fact that the Short-Time Fourier Transform (STFT) of noisy speech signals is sparse in the sense that transformed speech signals can be represented by a relatively small number of coefficients with large amplitudes in the time-frequency domain. The E-DATE estimator is robust in that it does not require prior information about the signal probability distribution except for the weak-sparseness property. In comparison to other state-of-the-art methods, the E-DATE is found to require the smallest number of parameters (only two). The performance of the proposed estimator has been evaluated in combination with noise reduction and compared to alternative methods. This evaluation involves objective as well as pseudo-subjective criteria.

Vibration Signal Analysis of High Voltage Circuit Breakers System Based on Hybrid Model of Power Spectrum Estimation

This paper discusses several methods of power spectrum estimation, respectively using the indirect method and Welch method to estimate power spectrum distribution through the acquisition of vibration signals of circuit breakers, and use the AR hybrid model to estimate different orders of power spectrum estimation. Under different length of data, the or- der selection method can be determined. Power spectrum estimation method is a power spectrum calculation method of vibration or random signal. It is an important link in the signal analysis, which can be used to detect the periodic component and the percentage of the sig- nal (1). And it is the great significance for the diagnosis of mechanical failure. Using a given set of sample data to esti- mate the power spectral density of a stationary random sig- nal is called power spectrum estimation. Power spectral den- sity is the discrete time Fourier transform of the random sig- nal autocorrelation sequence, it is used to characterize the statistical properties of random signals. The power spectrum of random signal reflects its frequency components and the relative strength of each component, it can reveal the signal rhythm from the frequency domain, and it is the important characteristic of random signals. Found in the study, statistical properties of CB vibration signal under different working conditions, the results show that when the circuit breaker is in normal working condition or state change is very small, the action of mechanical vibra- tion signal can be approximated at a stationary random pro- cess, which is consistent with the AR hybrid model assump- tion is usually described by. Therefore, the AR hybrid mod- el power spectrum estimation can achieve the desired effect of vibration signal.

Study of Autoregressive (AR) Spectrum Estimation Algorithm for Vibration Signals of Industrial Steam Turbines

Spectral analysis of the vibration signals of industrial steam turbines provides efficient reference for the characterization and discrimination of turbine faults. Conventional power spectrum estimation methods often exhibit contradiction between variance performance and resolution, leading to poor estimation results. In this study, we investigated Levision-Durbin recursive algorithm, Burg algorithm and periodogram power spectrum estimation algorithm, and also chose Akalke Information Criterion (AIC) to identify the optimal order p. Based on MATLAB, we wrote a simulation program for Autoregressive (AR) spectrum estimation algorithm and designed a graphic user interface, formulating the AR spectrum estimation algorithm program for vibration signals of industrial steam turbines. After field measurement of a steam turbine with sampling number of 400 and frequency of 256Hz, as well as order of 10 and 80, simulation was performed. It was demonstrated that AIC provides efficient reference for the identification of proper order. With the optimal order, AR spectrum estimation algorithm produces good variance performance and resolution, providing reference for the spectral analysis of vibration signals of industrial steam turbines.

In Situ Regolith Bulk Density Measurement for a Coiling-Type Sampler

AbstractMeasuring regolith density in outer space has always been a difficult problem. In this paper, a bulk density measuring method for planetary regolith based on vibration is proposed. The bulk density measurement of planetary regolith being sampled is realized mainly by vibration signal processing and general regression neural network identification. The method makes full use of the special structure of a mini coiling-type sampler, which utilizes the flexible coiling spring as its sampling arm. To begin, vibration signals are acquired and their trends are removed. Then, multiresolution wavelet analysis is used to filter out the signals that are not related to bulk density. After that, two of the power spectrum estimation methods, Periodogram and Welch, are introduced to extract the density-related features of the vibration signals and reduce the dimensions of them. The multiclass classification support vector machine is then adopted to verify the relationship between the bulk densities of regoliths a...

Sampling head–rock contact identification for regolith sampling in space

The success rate of planet sampling can be greatly increased if rocks at the sampling spot can be identified when the sampling head touches them and instead another place is chosen for sampling. In this paper a vibration sampling method is proposed to identify such sampling head–rock contact situation. Based on a flexible coiling-type sampling arm, the vibration sampling method is developed to achieve the rock contact identification for the sampling head. Firstly, the vibration signals are generated by a vibration motor and collected via an acceleration sensor; secondly, the multi-resolution wavelet is used to filter the vibration signals, and two of the power spectrum estimation methods, Welch and Burg, are introduced to extract the features of the vibration signals; at last, whether the sampling head is touching the rock is determined by the K Nearest Neighbors. The experiments show that the sampling head–rock identification problem can be achieved successfully by adopting the vibration sampling method, and in addition, the Burg method is verified to be the better method for the identification. The technique described in the paper is very useful for achieving a better sampling mission in space.