Generalized Cross-correlation Method Research Articles

Enhanced Method for Localization of Partial Discharges in Oil-Filled Transformers Using Acoustic Emission Signals

The accurate measurement of partial discharges (PD) in power transformers is a critical component in identifying faults and planning effective maintenance strategies. Acoustic emission (AE) sensing technology is suitable for detecting partial discharges in transformers because it is less affected by external electromagnetic interference and detects PD non-invasively. This paper delves into examining the relationship between the detection intensity, the specific type of PD source, and the distance to the discharge source, using AE sensors. It was observed that AE wave intensities from corona discharges tend to be relatively strong, indicating a higher detection probability. Creepage discharges usually exhibit the next level of intensity, followed by the PD occurring in bubbles. It is considered that the intensity of the AE waves varies significantly depending on both the speed at which the discharge propagates and the medium and volume of the discharge space. Furthermore, this study has conducted experimental comparisons among three distinct methods for calculating the Time Difference of Arrival (TDOA) in localization calculations. These methods are the energy criterion, Generalized Cross-Correlation (GCC), and GCC with Phase Transformation (PHAT). The experimental results suggest that the energy criterion method is particularly effective when sensors are distributed placement around the entire tank. In contrast, the GCC-PHAT method shows greater suitability in scenarios where sensors are centralized placement in certain sections of the tank. It was clearly observed that the GCC-PHAT method, which suppresses the impact of noise and reflections, consistently achieves higher estimation accuracy in comparison to the standard GCC method. Notably, this method has shown the ability to maintain its accuracy levels even in cases of low discharge intensities. The implications of these findings are significant, as they could improve the precision and effectiveness of maintenance diagnostics in power transformers.

Study of Localization of Partial Discharges in Oil-filled Transformers using Acoustic Emission Signals

The measurement of partial discharges (PD) in power transformers is crucial for fault detection and maintenance scheduling. In this paper, the relationship between detection intensity, type of PD source and propagation distance is investigated using an acoustic emission (AE) sensor. The AE wave intensity by the corona discharges are relatively strong. Creeping discharges were next, followed by PD in bubbles. Furthermore, two methods for calculating time difference of arrival (TDOA) in locate calculations, energy reference and generalized cross-correlation (GCC), were experimentally compared. The results showed that the energy reference method is suitable when sensors can be placed around the tank, while the GCC method is suitable when sensors are concentrated in specific parts of the tank. This finding may contribute to improving the accuracy of maintenance diagnostics.

A Sound Source Localization Method Based on Frequency Divider and Time Difference of Arrival

In recent years, sound source localization, as a passive localization technique with higher safety and convenience compared with other localization techniques such as active emission of electromagnetic waves, has received more and more attention in academia. This paper researches and improves the far-field sound source localization algorithm based on the generalized cross-correlation method (GCC) Time Difference of Arrival (TDOA) estimation algorithm and completes the design and implementation of the microphone array sound source localization system. This paper adds a frequency divider to the traditional generalized correlation time delay estimation algorithm for pre-processing, sampling, and localization of sound source acoustic waves and adopts a low-cost microphone array deployment scheme as far as possible to improve the flexibility and practicality of the localization system; at the same time, the “Minimum Sphere Method” is used at the back end of the algorithm to classify the localization coordinates at different frequencies and, finally, output reasonable sound source coordinates. In the back-end of the algorithm, the “Minimum Sphere Method” is used to classify the localization coordinates at different frequencies and, finally, output the reasonable sound source coordinates. The experimental results show that the sound source localization system designed in this paper has good performance in terms of localization accuracy and cost-effectiveness and overcomes the failure of the generalized mutual correlation algorithm in the original application of high noise environment and multi-source environment localization.

A Practice-Distributed Thunder-Localization System with Crowd-Sourced Smart IoT Devices.

Lightning localization is of great significance to weather forecasting, forest fire prevention, aviation, military, and other aspects. Traditional lightning localization requires the deployment of base stations and expensive measurement equipment. With the development of IoT technology and the continuous expansion of application scenarios, IoT devices can be interconnected through sensors and other technical means to ultimately achieve the goal of automatic intelligent computing. Therefore, this paper proposes a low-cost distributed thunder-localization system based on IoT smart devices, namely ThunderLoc. The main idea of ThunderLoc is to collect dual-microphone data from IoT smart devices, such as smartphones or smart speakers, through crowdsourcing, turning the localization problem into a search problem in Hamming space. We studied the dual microphones integrated with smartphones and used the sign of Time Difference Of Arrival (TDOA) as measurement information. Through a simple generalized cross-correlation method, the TDOA of thunderclaps on the same smartphone can be estimated. After quantifying the TDOA measurement from the smartphone node, thunder localization was performed by minimizing the Hamming distance between the binary sequence and the binary vector measured in a database. The ThunderLoc system was evaluated through extensive simulations and experiments (a testbed with 30 smartphone nodes). The extensive experimental results demonstrate that ThunderLoc outperforms the main existing schemes in terms of effectively locating position and good robustness.

Ultra-short baseline positioning delay estimation based on signal preprocessing and fourth-order cumulant

To address the problem that the traditional generalized cross correlation (GCC) method in ultra-short baseline (USBL) positioning systems has a poor delay estimation accuracy in a low signal-to-noise ratio environment or complex noise background, a generalized quadratic cross correlation (GQCC) time delay estimation algorithm based on signal preprocessing and fourth-order cumulants is proposed. The noisy signal was first preprocessed using singular value decomposition and wavelet denoising. Then, the delay was calculated using an algorithm that combined the GQCC and the fourth-order cumulant. The results of the simulation and sea trial demonstrate that the proposed method is superior to the GCC method and the GQCC method and may significantly increase the positioning accuracy of the USBL system. This technique can offer a fresh technological perspective for weak signal detection and passive positioning of small targets in ocean detection.

Signal Modeling of Permanent Magnet Sodium Flowmeter With Embedded Vortex Generator Based on Transit Time Series

The permanent magnet sodium flowmeter with embedded vortex generator (PMSF-VG) monitors the liquid metal sodium flow rate in real time in order to ensure the safe operation of the fast neutron reactor. The previous literature studied how to improve the in-situ calibration accuracy of the flowmeter from the perspective of signal processing. This paper studies this issue from the perspective of signal source. According to the experimental data, the transit time series is calculated by using the generalized cross-correlation method, its characteristics are analyzed, and a moving average (MA) model is established for predicting and improving the accuracy of the in-situ calibration. This model is composed of a steady-state component and a fluctuation component. The former reflects the ideal value of the cross-correlation flow rate in the in-situ calibration, and is related to the meter linearity and indication error, which determines the upper limit of the measurement accuracy. The latter determines the repeatability error of the in-situ calibration. Aiming at its feature, a two-stage moving average filtering method is adopted to reduce the repeatability error of measuring the transit time and improve the accuracy of the in-situ calibration.

Small window cross-correlation accumulation time difference algorithm based on peak judgment method and generalized cross-correlation method

Based on peak judgment method and generalized correlation method, this paper proposes a new ultra high frequency partial discharge signal time difference algorithm. This algorithm avoids the defect that the traditional cross-correlation algorithm has a large correlation error for signals with lower amplitude. At the same time, by accumulating the correlation results of N groups of data, the influence of random interference of UHF PD signal on the correlation results is reduced. N times of correlation analysis were performed and correlation results were accumulated in a small area near the peak of the UHF PD signal, not only avoiding the deficiencies of the traditional peak judgment method that the peak value of the UHF PD signal is not easy to determine, but also minimizing the effect of PD white noise. In order to verify the effectiveness of the proposed algorithm, the time difference calculated by different algorithm are compared, and the results show that the proposed algorithm is superior to other existing algorithms.

Research on Underwater Sound Source Localization Based on NLM-EEMD and FCM-Generalized Quadratic Correlation

The accuracy of underwater sound source localization depends on the preprocessing effect of signal denoising and delay estimation performance. In order to meet the needs of high-precision underwater positioning, this article studies a time delay estimation method based on nonlocal mean-enforced mean empirical mode decomposition (NLM-EEMD) autocorrelation denoising preprocessing and fuzzy C-means clustering (FCM)-generalized quadratic for underwater sound source localization technology. The theories such as NLM, EEMD, FCM, and generalized quadratic correlation delay estimation are studied in detail, and the speech signal of the test library in the TIMIT standard library is selected for simulation analysis, which verifies the correctness of the delay estimation method. Experiments were carried out in a larger outdoor pool. The analysis results show that the NLM-EEMD adaptive denoising method effectively reduces noise interference and improves the quality of EEMD decomposition and the FCM-generalized quadratic correlation method increases the correlation between two signals. The time delay estimation accuracy is improved, and the positioning accuracy is 48.66% higher than that of the direct generalized cross-correlation method.

A Hybrid Leak Localization Approach Using Acoustic Emission for Industrial Pipelines

Acoustic emission techniques are widely used to monitor industrial pipelines. Intelligent methods using acoustic emission signals can analyze acoustic waves and provide important information for leak detection and localization. To address safety and protect the operation of industrial pipelines, a novel hybrid approach based on acoustic emission signals is proposed to achieve reliable leak localization. The proposed method employs minimum entropy deconvolution using the maximization kurtosis norm of acoustic emission signals to remove noise and identify important feature signals. In addition, the damping frequency energy based on the dynamic differential equation with damping term is designed to extract important energy information, and a smooth envelope for the feature signals over time is generated. The zero crossing tracks the arrival time via the envelope changes and identifies the time difference of the acoustic waves from the two channels, each of which is installed at the end of a pipeline. Finally, the time data are combined with the velocity data to localize the leak. The proposed approach has better performance than the existing generalized cross-correlation and empirical mode decomposition combined with the generalized cross-correlation methods, providing proper leak localization in the industrial pipeline.

A New Adaptive GCC Method and Its Application to Slug Flow Velocity Measurement in Small Channels.

In this work, an adaptive generalized cross-correlation (AGCC) method is proposed that focuses on the problem of the conventional cross-correlation method not effectively realizing the time delay estimation of signals with strong periodicity. With the proposed method, the periodicity of signals is judged and the center frequencies of the strongly periodical components are determined through the spectral analysis of the input signals. Band-stop filters that are used to suppress the strongly periodical components are designed and the mutual power spectral density of the input signals that is processed by the band-stop filters is calculated. Then, the cross-correlation function that is processed is the inverse Fourier transform of the mutual power spectral density. Finally, the time delay is estimated by seeking the peak position of the processed cross-correlation function. Simulation experiments and practical velocity measurement experiments were carried out to verify the effectiveness of the proposed AGCC method. The experimental results showed that the new AGCC method could effectively realize the time delay estimation of signals with strong periodicity. In the simulation experiments, the new method could realize the effective time delay estimation of signals with strong periodicity when the energy ratio of the strongly periodical component to the aperiodic component was under 150. Meanwhile, the cross-correlation method and other generalized cross-correlation methods fail in time delay estimation when the energy ratio is higher than 30. In the practical experiments, the velocity measurement of slug flow with strong periodicity was implemented in small channels with inner diameters of 2.0 mm, 2.5 mm and 3.0 mm. With the proposed method, the relative errors of the velocity measurement were less than 4.50%.

Plastic Pipeline Leak Localization Based on Wavelet Packet Decomposition and Higher Order Cumulants

This paper presents a plastic pipeline leak localization method based on wavelet packet decomposition and higher order cumulants. The hydrophone sensors are used to capture the acoustic signals generated by the water leakage in the plastic pipe. The feature frequency band containing the most leak information is subsequently extracted by performing wavelet packet decomposition on the signals. The most suitable wavelet packet basis function for leak signals is obtained by comparing the effects of varieties of wavelet packet basis functions. Furthermore, the higher order cumulant method is used to estimate the time delay between the two sensors signals, from which the leak hole can be located. The simulation and experimental results show that, compared with the basic cross-correlation method (BCC) and the generalized cross-correlation method (GCC), the higher order cumulant method has higher leak localization accuracy. The relative error of the third-order cumulant method is 2.99% in experiment 1 and 7.58% in experiment 2, while the relative localization error of the fourth-order cumulant method is 1.96% in experiment 1 and 1.23% in experiment 2.

Audiovisual speaker indexing for Web-TV automations

The current paper introduces a multimodal framework to provide Web-TV automations for live broadcasting and overall big streaming data management. The term indexing refers to the spatiotemporal localization of speakers participating in a discussion panel. Multiple modalities acting in parallel form the data-driven decision-making pipeline. The automated workflow includes the tasks of active speaker detection and localization, frame selection, and creation of a semantically annotated database. For improved performance and robustness, an information fusion model is proposed, which makes use of different audio and visual modalities. Audio-driven Voice Activity Detection follows the Enhanced Temporal Integration methodology applied on a standard audio feature set. For the localization of the dominant audio source, the argument that maximizes the General Cross-Correlation method is calculated. The visual modalities include face and mouth detection and Visual Voice Activity Detection. A Long Short Term Memory network is trained with mouth image sequences to determine voice activity. The values of the audio and visual Voice Activity Detection modules, as well as the General Cross-Correlation result, are used to train an Adaptive Neuro-Fuzzy model, which is responsible for the final decision. Experimental results prove the superiority of the information fusion approach compared to unimodal audio or visual models.

Retrieving reflection arrivals from passive seismic data using Radon correlation

AbstractSince explosive and impulsive seismic sources such as dynamite, air guns, gas guns or even vibroseis can have a big impact on the environment, some companies have decided to record ambient seismic noise and use it to estimate the physical properties of the subsurface. Big challenges arise when the aim is extracting body waves from recorded passive signals, especially in the presence of strong surface waves. In passive seismic signals, such body waves are usually weak in comparison to surface waves that are much more prominent. To understand the characteristics of passive signals and the effect of natural source locations, three simple synthetic models were created. To extract body waves from simulated passive signals we propose and test a Radon-correlation method. This is a time-spatial correlation of amplitudes with a train of time-shifted Dirac delta functions through different hyperbolic paths. It is tested on a two-layer horizontal model, a three-layer model that includes a dipping layer (with and without lateral heterogeneity) and also on synthetic Marmousi model data sets. Synthetic tests show that the introduced method is able to reconstruct reflection events at the correct time-offset positions that are hidden in results obtained by the general cross-correlation method. Also, a depth migrated section shows a good match between imaged horizons and the true model. It is possible to generate off-end virtual gathers by applying the method to a linear array of receivers and to construct a velocity model by semblance velocity analysis of individually extracted gathers.

Localization and intensity calibration of partial discharge based on attenuation effect of ultrasonic sound pressure

In the insulation system of power equipment, the partial discharge (PD) of short period does not cause the insulation to produce the penetrating breakdown, however the long-term PD of is one of the important causes of local deterioration, and even breakdown in dielectric. Therefore, it is very important to study the location of PD source and the calibration of discharge intensity. To achieve this, in this paper we take the needle-plate discharge model for example and go through the following steps respectively. Firstly, combined with the positive correlation between the ultrasonic signal and the apparent discharge magnitude in the process of PD, the ultrasonic method to detect partial discharge can be implemented. Then, based on the principle of time difference of arrival method (TDOAM), the accuracy of location is analyzed by using quantum genetic algorithm (QGA), genetic algorithm (GA), simulated annealing algorithm (SAA), particle swarm optimization (PSO) and generalized cross correlation method (GCC), respectively. And thus, starting from the study of the attenuation effect of sound pressure caused by the propagation loss, reflection and refraction of acoustic wave, the calibration model of PD intensity is established for the first time after determining the location of PD source with high precision. Some important findings are extracted from simulations and experimental results. First, the localization algorithm of PD source with high precision is observed. The localization of PD source by means of QGA is the most accurate, with maximum deviation of (0.27 ± 0.13) cm. Comparing with GA, SAA, PSO and GCC, the accuracy of location is improved by 33.57%, 41.51%, 32.11% and 87.26%, respectively. Second, due to the attenuation effect of sound pressure, when the measured voltage amplitude of ultrasonic signal is the same, the apparent discharge magnitude of PD source gradually increases with the test distance increasing. When the test distance is 37.80 cm, the apparent discharge magnitude of PD source is 633.83 pC, which increases by 28.51% compared with 7.00 cm. Moreover, simulation results and measurement results are compared with each other and they are well consistent. The discharge curve almost coincides with the calibration fitting curve of PD source when the test distance is 7.00 cm. Finally, it is concluded that the discharge intensity calibration model of PD source is accurate, which is of great significance in evaluating the extent of insulation damage.

A novel bat algorithm with double mutation operators and its application to low-velocity impact localization problem

The low-velocity impact localization in the plate structure of the ship is a critical problem which can be considered as a nonlinear optimization problem. The bat algorithm (BA) has been widely used to solve nonlinear optimization problems. However, the standard BA exhibits poor performance on complex problems because of its premature convergence. In this study, a novel bat algorithm with double mutation operators (TMBA), in which a modified time factor and two mutation operators are integrated, is proposed to enhance BA’s performance on nonlinear optimization problems. Classical benchmark functions are employed to analyze the contributions of the three modifications and demonstrate the significant improvement of TMBA. For the low-velocity impact localization problem, the low-velocity impact localization system based on fiber Bragg grating (FBG) sensors is utilized to receive the impact signals. The wavelet threshold de-noising method and the generalized cross-correlation method are both applied to the extraction of time differences between the impact signals. Then, the proposed algorithm and several well-known optimization algorithms are adopted to solve the minimization fitness function which is established using the triangulation method. The statistical results indicate that TMBA is more feasible and effective for solving the low-velocity impact localization problem.

A Miniature Four-Microphone Array for Two-Dimensional Direction-of-Arrival Estimation Based on Biomimetic Time-Delay Magnification

A miniature microphone array based on interaural time difference (ITD) is designed. This array contains four microphones with certain arrangement and aims for two-dimensional (azimuth and elevation) direction-of-arrival (DOA) estimation in the whole three-dimensional space. The array can be small because it uses a coupling algorithm that magnifies the time delay between the signals received by every two microphones. The coupling algorithm is built according to a pairwise-coupled multidimensional mechanical model inspired by the ears of the tiny parasitoid fly Ormia ochracea. It was verified that the time-delay magnification can be independent of the incident angle when the parameters in the model satisfy specific relationships. This paper further investigates the multidimensional coupled system and advocates to realize the magnification mechanism in algorithm, where the physical parameters can change according to sound frequency to ensure the time-delay magnification. Moreover, the arrangement of microphones is specially designed to help the array to achieve similar measuring accuracy for all directions in the three-dimensional space. Corresponding signal process procedures are also provided. Simulations that use such an array to estimate the azimuth and elevation angles of sound source are performed via general cross-correlation (GCC) method. Results verify the feasibility of the microphone array and show that the accuracy of the estimation increases after the signals are processed by the coupled system.

Time delay estimation based on log-sum and lp-norm penalized minor component analysis

Time-delay estimation (TDE), which measures the relative time delay between different receivers, is a fundamental approach for identifying, localizing, and tracking radiating sources. The generalized cross-correlation method is the most popular and is well explained in a landmark paper by Knapp and Carter [(1976). IEEE Trans. Acoust. Speech Signal Process. 24(4), 320-327]. Adaptive eigenvalue decomposition- (EVD) based algorithms have also been developed to improve TDE performance, especially in reverberant environments. This paper extends the adaptive EVD algorithm to utilize the sparsity in transfer channel between source and receivers. Two estimation algorithms based on the log-sum and lp-norm penalized minor component analysis by excitatory and inhibitory learning rules is proposed. In addition, simulations with uncorrelated, correlated noise and reverberation for several signal-to-noise ratios are performed to show the improved estimation performance in noise and reverberation.

A Biologically Inspired Coupled Microphone Array for Sound Source Bearing Estimation

The Ormia ochracea, a species of parasitic fly, has a remarkable localization ability despite the tiny interaural distance compared with the incoming wavelength. The mechanical coupling between its ears enhances the differences of the two received signals, the main cues to locate the source. Inspired by the coupling mechanism, we present a miniature coupled two-microphone array for estimating sound source horizontal bearing. The coupled array consists of a standard two-microphone array and a two-input, two-output filter which implements the coupling. The relationship between filter parameters and time delay magnification is investigated to provide theoretical support for array design. With appropriate parameters, the time delay of received signals can be linearly magnified. Based on the linear magnification, we present a method for estimating source direction using the coupled array. The influence of time delay magnification on time delay estimation accuracy is explored through the general cross-correlation (GCC) method. Experiments are conducted to verify the coupled array and demonstrate its advantages on improving the resolution of estimation of time delay and accuracy of bearing estimation compared with the standard array with the same element spacing.

Parameter study of time-delay magnification in a biologically inspired, mechanically coupled acoustic sensor array.

Sound source localization uses interaural time difference (ITD) or interaural intensity difference cues, and most of the methods based on ITD are limited by the aperture of array. However, a kind of parasitoid fly called Ormia ochracea with coupled ears has a remarkable ability to localize calling of crickets regardless of the fly's small body size. The structure of fly's ear is generalized, and a multi-dimensional coupled system with an acoustic sensor array is proposed. The magnification factor of the phase difference in this system, which can be used to describe the ITD changes of signals from the coupled system, is chosen as the kernel parameter to measure the effect of coupling. The coupled system is optimized by choosing appropriate physical parameters such that the degree of magnification does not vary with angle of incidence. The simulation results demonstrate that the time delay between two signals increases by the coupled system, and the magnification factor remains stable as expected. Compared with the traditional general cross-correlation method, the localization error of the coupled system is reduced.

Real-time time difference of arrival estimation for moving wideband sources

The modified generalized cross-correlation method is used for finding the time difference of arrival (TDOA) and resolving the angle of arrival toward moving targets in real-time. It addresses the decorrelation due to differential Doppler effect leading to relative time companding (RTC) between sensors. The proposed method efficiently approximates the RTC prior to the computation of cross-correlation, thus working faster than methods using maximization of the passive ambiguity function and other approximation methods. The algorithm is based on computing the spectrum of deskewed short-time cross-correlator (DSTC) in the frequency domain by averaging a short-term spectrogram computed on overlapped segments of analysis in a time window. The same spectrogram is used to compute magnitude-squared coherence (MSC), then by maximizing the average MSC in the band of interest, the RTC is found. It is used to compute a single inverse Fourier transform. This method is applied to data collected by the Acoustic Aircraft ...