Adaptive Singular Value Decomposition Research Articles

An adaptive spatiotemporal filter for ultrasound localization microscopy based on density canopy clustering

Ultrasound Localization Microscopy (ULM) facilitates structural and hemodynamic imaging of microvessels with a resolution of tens of micrometers. In ULM, the extraction of effective microbubble signals is crucial for image quality. Singular Value Decomposition (SVD) is currently the most prevalent method for microbubble signal extraction in ULM. Most existing ULM studies employ a fixed SVD filter threshold using empirical values which will lead to imaging quality degradation due to the insufficient separation of blood signals. In this study, we propose an adaptive and non-threshold SVD filter based on canopy-density clustering, termed DCC-SVD. This filter automatically classifies the components of the SVD based on the density of their spatiotemporal features, eliminating the need for parameter selection. In in vitro tube phantom, DCC-SVD demonstrated its ability to adaptive separation of blood and bubble signal at varying microbubble concentrations and flow rates. We compared the proposed DCC-SVD method with the Block-match 3D (BM3D) filter and a classical adaptive method called spatial similarity matrix (SSM), using concentration-variable in vivo rat brain data, as well as open-source rat kidney and mouse tumor datasets. The proposed DCC-SVD improved the global spatial resolution by approximately 4 μm from 30.39 μm to 26.02 μm. It also captured vessel structure absent in images obtained by other methods and yielded a smoother vessel intensity profile, making it a promising spatiotemporal filter for ULM imaging.

Enhanced extended-input LSTM with an adaptive singular value decomposition UKF for LIB SOC estimation using full-cycle current rate and temperature data

Accurately estimating the state of charge (SOC) of lithium-ion batteries by the battery management system (BMS) is crucial for efficient energy management and power distribution control in electric vehicles (EVs). By far, data-driven methods have been extensively used in estimating the SOC of lithium-ion batteries to efficiently operate EVs with limited resources. However, the learning ability of these methods needs further enhancement. Therefore, this paper aims to analyze the SOC performance of an extended-input long short-term memory (ELSTM) model. The inputs of the ELSTM are improved with identified battery parameters based on an adaptive multi-timescale identification method with frequency difference decomposition. Second, an adaptive singular value decomposition-transformed unscented Kalman filter (ASVDUKF) is proposed to integrate all unknown variables and guarantee the positive definiteness of the error covariance matrix into a vector to form a multi-fusion model that robustly outputs the denoised and optimized SOCs based on the estimations of the ELSTM. The SOC results demonstrate that the ELSTM outperforms the LSTM, which uses conventional input data. Following that, the ELSTM-ASVDUKF model ensures high accuracy and stability with optimal mean absolute error, root-mean-square error, and mean absolute percentage error of 0.0939%, 0.1074%, and 0.1257%, respectively. The proposed model is validated using various full-cycle current rate and temperature data from two different batteries, establishing it as a promising solution for future development.

A Combined Noise Reduction Method for Floodgate Vibration Signals Based on Adaptive Singular Value Decomposition and Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

To address the issue of the vibration characteristic signals of floodgates being affected by background white noise and low-frequency water flow noise, a noise reduction method combining the improved adaptive singular value decomposition algorithm (ASVD) and the improved complete ensemble EMD with adaptive noise (ICEEMDAN) is proposed. Firstly, a Hankel matrix is constructed based on the collected discrete time signals. After performing SVD on the Hankel matrix, the ASVD algorithm is used to automatically select the effective singular values to filter out most of the background white noise and retain the useful frequency components with similar energy in the signal. Then, ICEEMDAN combined with the Spearman correlation coefficient method is used to further filter out residual white noise and low-frequency water flows. The noise reduction performance of this combined method is verified through simulation experiments. Filtered by the ASVD-ICEEMDAN method, the signal-to-noise ratio of the simulation signal (50% noise level) is increased from 4.417 to 16.237, and the root mean square error is reduced from 2.286 to 0.586. Based on the practically measured vibration signals of a floodgate at a large hydropower station, the result shows that the ASVD-ICEEMDAN method exhibits good noise reduction performance and feature information extraction abilities for floodgate vibration signals, and can provide support for operational mode analysis and damage identification of practical structures under complex interference conditions.

Chaotic signal denoising based on energy selection TQWT and adaptive SVD.

Aiming at the problem of denoising chaotic signals with low signal-to-noise ratio and unknown dynamic system parameters, a new chaotic signal denoising algorithm is proposed, which combines adjustable Q-factor wavelet transform (TQWT) and adaptive singular value decomposition (ASVD). This method uses TQWT to decompose the noisy chaotic signal. According to the maximum wavelet entropy theory and energy threshold rule, the subband of TQWT is accurately divided into signal subband and noise subband. For noise subbands, adaptive SVD is used to denoise them, to achieve preliminary denoising. In ASVD, the standard deviation of the singular value subset is used to determine the effective reconstruction order to improve the noise suppression effect. To further remove noise in the signal subband, TQWT reconstruction is performed on the preliminarily denoised signal, and ASVD is used to denoise the reconstructed signal again to obtain the chaotic signal after secondary denoising. Chua's simulated signal and four kinds of underwater radiated noise measured by TQWT-ASVD were denoised, and compared with the SVD denoising method, TQWT denoising method, complete ensemble empirical mode decomposition with adaptive noise and threshold denoising method (CEEMDAN-WT) and modified ensemble empirical mode decomposition combined with least squares denoising method (MEEMD-LMS), The experimental results show that the TQWT-ASVD method can reduce the noise of chaotic signals more effectively. Compared with SVD, TQWT, CEEMDAN-WT, MEEMD-LMS, and Chua's signal denoising method, the signal-to-noise ratio (SNR) of this method increased by 23.22%, 26.46%, 18.79%, 16.11% the root mean square error (RMSE) decreased by 32.53%,39.48%, 30.96%, 27.94%, and the row entropy (PE) decreased by 40.44%, 41.96%, 22.78%, 20.59%; After reducing the radiation noise of cargo ships, the PE value of this method is reduced by 13.91%, 10.18%, 10.88%, 8.68% respectively, and the FE value is reduced by 33.66%, 31.42%, 26.98%, 21.32% respectively.

Visualization and quantitative evaluation of delamination defects in GFRPs via sparse millimeter-wave imaging and image processing

Multilayer glass fiber reinforced polymer (GFRP) has broad applications, playing a pivotal role in various fields including aerospace and wind energy production. These structures may develop internal delamination defects during their manufacture or service, which can significantly impair the integrity and safety of the equipment. Thus, the inspection of such delamination defects during the service life of the equipment is crucial for assessing its operational performance and lifespan. Millimeter-wave (MMW) nondestructive testing (NDT) presents a promising method for detecting these defects in GFRPs, due to its high resolution and powerful penetration characteristics. This paper proposes a visual NDT and quantitative characterization method based on sparse imaging and image processing. This method encompasses the following three key points. (i) To facilitate rapid detection and visualization in monostatic mode, we proposed a detection approach utilizing sparse scanning and imaging. Initially, we developed a sparse sampling control matrix grounded to guide the probe for spatial automatic sparse sampling, thereby enhancing data acquisition efficiency and reducing data volume. Subsequently, we introduced a rapid sparse imaging method, integrating wave spectrum reconstruction and compressed sensing (CS). Beginning from the original data domain, we designed a fast imaging operator employing forward and inverse transformations of spectrum reconstruction imaging. (ii) In order to further refine the quality of the detection images, we proposed an adaptive singular value decomposition (SVD) approach to suppress the coupling signal and enhance the specimen signal. By adaptively adjusting the singular values, we generated a signal with the optimal target-to-background ratio (TBR). (iii) We ascertained the position and area parameters of the delamination defects using the proposed defect quantification method, combining image segmentation and image statistics. Experimental validation affirmed the effectiveness of the proposed method, revealing the following results: 1) The proposed method, despite utilizing a small amount of observational data, generates better visual images while maintaining efficient detection; 2) It can effectively identify all 0.5 mm-thick internal delamination defects in GFRPs, simultaneously retaining high quantitative characterization precision; 3) The positional quantification error remains less than 0.5 mm, and the relative area quantification error is constrained within 8%.

Investigation of urban active faults using the short-offset transient electromagnetic method: an example from Shunping, Hebei Province, China

Abstract The presence of an urban active fault carries a geological hazard risk that threatens urban construction. Therefore, detecting such faults is crucial for mitigating potential risks and ensuring safe urban development. The grounded-wire source short-offset transient electromagnetic method (SOTEM) offers an opportunity to identify faults with high-conductivity. The results of a SOTEM survey conducted in Shunping, Hebei Province, were presented to detect buried faults and locate the industrial park. Data were analysed using the one-dimensional inversion method with SOTEMsoft software, which was denoised using the short-time sliding window adaptive singular value decomposition algorithm based on the wavelet threshold. Despite the challenges posed by urban features, particularly traffic, the survey results located the fault and hold significant potential to reveal its extent and depth. The study highlights the importance of avoiding fault sites during urban construction, even though the area is currently tectonically stable. This paper reveals an unusual opportunity to study the effectiveness of electromagnetic methods for detecting buried faults in cities and reveal geological structures covered by Quaternary formations.

Tire-road friction coefficient estimation for automatic guided vehicle under multiple road conditions

The traditional unscented Kalman filter (UKF) will have the problem of reduced accuracy or even divergence in the estimation process due to state model perturbation, unknown noise of the system, and other factors, which in turn affect the estimation results of the tire-road friction coefficient. By this problem, the paper investigates the tire-road friction coefficient estimation by taking an automatic guided vehicle (AGV) as the research object and proposes an adaptive singular value decomposition unscented Kalman filter (ASVD-UKF) with a noise estimator. Singular value decomposition (SVD) is introduced into the unscented Kalman filter (UKF) for Sigma sampling to suppress the negative definiteness of the state covariance matrix in UFK. The paper considered estimation schemes for joint road, μ-split road, and μ-different road and constructed corresponding ASVD-UKF observers to reduce the dimension of the road estimation model and real-time observation of four tire-road friction coefficients. Results show that the average absolute error of the μ-split road, joint road, and μ-different road proposed in this paper is significantly smaller than that of UFK, and the estimation accuracy is improved by 13.39%, 6.74%, and 5.71%, respectively. A Distributed Drive AGV prototype was developed for a real vehicle verification experiment, with only a 1.14% error between simulation and experiment. It is further proved that the designed observers are practical. The research can provide a theoretical basis and experimental foundation for the tire-road friction coefficient estimation.

Dual-mode adaptive-SVD ghost imaging.

In this paper, we present a dual-mode adaptive singular value decomposition ghost imaging (A-SVD GI), which can be easily switched between the modes of imaging and edge detection. It can adaptively localize the foreground pixels via a threshold selection method. Then only the foreground region is illuminated by the singular value decomposition (SVD) - based patterns, consequently retrieving high-quality images with fewer sampling ratios. By changing the selecting range of foreground pixels, the A-SVD GI can be switched to the mode of edge detection to directly reveal the edge of objects, without needing the original image. We investigate the performance of these two modes through both numerical simulations and experiments. We also develop a single-round scheme to halve measurement numbers in experiments, instead of separately illuminating positive and negative patterns in traditional methods. The binarized SVD patterns, generated by the spatial dithering method, are modulated by a digital micromirror device (DMD) to speed up the data acquisition. This dual-mode A-SVD GI can be applied in various applications, such as remote sensing or target recognition, and could be further extended for multi-modality functional imaging/detection.

Pressure vessel leakage detection method based on online acoustic emission signals

Pressure vessel leakages cannot initially be visited directly and will gradually cause deterioration, which can result in catastrophic damage. Acoustic emission (AE) signals generated by leakage have the potential of being used for online monitoring. Unfortunately, AE signals have the characteristics of being non-stationary, wide-band and with strong noise interference, which causes the monitoring results to have low reliability. To address the poor robustness of traditional time-domain and time-frequency domain-based monitoring methods, an online monitoring method based on adaptive singular value decomposition (ASVD) is proposed in this paper. Firstly, singular value decomposition (SVD) is used to divide the signal space into a signal subspace and a noise subspace. Experiments indicate that SVD can distinguish leakages under conditions of different pressures and variable temperature, which means that SVD is sensitive to changes in signal. Subsequently, update iteration-based ASVD algorithms are proposed for long-term online health monitoring and ASVD is shown to be successful in distinguishing the different statuses of intact, leakage and repaired. To improve the robustness of ASVD, a novel energy indicator is proposed, which can identify the status change more effectively. With the proposed methodology, an online monitoring application for pressure vessel leakage detection is expected to be achievable.

Adaptive singular value decomposition filtering of high-intensity focused ultrasound interference enables real-time ultrasound-guided therapy

High-intensity focused ultrasound (HIFU) is an emerging therapeutic tool for the effective thermal ablation of pathological tissue. For accurate localization of the target and safe control of the HIFU dosage, real-time imaging guidance during the HIFU exposure is desired. Ultrasound imaging has the capability to guide clinicians toward a lesion in real time, but is not an ideal option, as HIFU application causes strong interference, thereby substantially distorting the images used for guidance. Thus, this study introduces singular value decomposition-based filtering capable of restoring ultrasound harmonic images from HIFU interference without undesirable spectral distortion. The results were experimentally validated with a custom-made phantom, indicating that this approach effectively eliminates HIFU-induced artifacts, which is essential for real-time monitoring of the therapeutic process.

A Novel Denoising Method for Partial Discharge Signal Based on Improved Variational Mode Decomposition

Partial discharge (PD) online monitoring is a common technique for high-voltage equipment diagnosis. However, due to field interference, the monitored PD signal contains a lot of noise. Therefore, this paper proposes a novel method by integrating the flower pollination algorithm, variational mode decomposition, and Savitzky–Golay filter (FPA-VMD-SG) to effectively suppress white noise and narrowband noise in the PD signal. Firstly, based on the mean envelope entropy (MEE), the decomposition number and quadratic penalty term of the VMD were optimized by the FPA. The PD signal containing noise was broken down into intrinsic mode functions (IMFs) by optimized parameters. Secondly, the IMFs were classified as the signal component, the noise dominant component, and the noise component according to the kurtosis value. Thirdly, the noise dominant component was denoised using the SG filter, and the denoised signal was mixed with the signal component to reconstruct a new signal. Finally, threshold denoising was used to eliminate residual white noise. To verify the performance of the FPA-VMD-SG method, compared with empirical mode decomposition with wavelet transform (EMD-WT) and adaptive singular value decomposition (ASVD), the denoising results of simulated and real PD signals indicated that the FPA-VMD-SG method had excellent performance.

Rolling Bearing Fault Diagnosis Based on Nonlinear Underdetermined Blind Source Separation

One challenge of bearing fault diagnosis is that the vibration signals are often a nonlinear mixture of unknown source signals. In addition, the practical installation position also limits the number of observed signals. Hence, bearing fault diagnosis is a nonlinear underdetermined blind source separation (UBSS) problem. In this paper, a novel nonlinear UBSS solution based on source number estimation and improved sparse component analysis (SCA) is proposed. Firstly, the ensemble empirical mode decomposition (EEMD), correlation coefficient (CC), and adaptive threshold singular value decomposition (ATSVD) joint approach is proposed to estimate the source number. Then, the observed signals are transformed into the time−frequency domain by short−time Fourier transform (STFT) to meet the sparsity requirement of SCA. The frequency energy is adopted to increase the accuracy of fuzzy C−means (FCM) clustering, so as to ensure the accuracy estimation of the mixing matrix. The L1−norm minimization is utilized to recover the source signals. Simulation results prove that the proposed UBSS solution can exactly estimate the source number and effectively separate the simulated signals in both linear and nonlinear mixed cases. Finally, bearing fault testbed experiments are conducted to verify the validity of the proposed approach in bearing fault diagnosis.

Adaptive singular value decomposition for bearing fault diagnosis under strong noise interference

Singular value decomposition (SVD) is an effective tool for analyzing the signals from mechanical systems and for fault diagnosis, which is a non-parametric signal analysis method free from phase shift and waveform distortion. In SVD, the embedding dimension of the Hankel matrix is an important parameter that directly influences the effectiveness of the SVD. However, the embedding dimension is usually determined by experience, which is quite subjective and limits the applicability of SVD. As such, a novel SVD method, named adaptive SVD (ASVD), is proposed in this paper. In ASVD, novel criteria are defined to obtain the specific embedding dimensions for different mechanical signals by means of numerical simulation. A novel phenomenon, that the singular value pairs change periodically with the step size of half-cycle sampling points, is found and it can be used to calculate specific embedding dimension instead of selecting it from a range using experience. Meanwhile, the envelope spectral amplitude ratio index is developed for addressing the issue of excessive decomposition in classic SVD. Lastly, an ASVD-based bearing fault diagnosis method is proposed to adaptively select useful sub-signals and to detect faults. Both simulated signal and experiment signals, collected from different bearing test rigs are used to verify the effectiveness of the proposed method. The results show that the proposed method has a satisfactory ability to eliminate interference noise and detect bearing fault.

Corrigendum

International Journal of Circuit Theory and ApplicationsEarly View CORRIGENDUMFree Access Corrigendum This article corrects the following: Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium-ion battery state-of-charge estimation Xiao Yang, Shunli Wang, Wenhua Xu, Jialu Qiao, Chunmei Yu, Carlos Fernandez, Volume 50Issue 2International Journal of Circuit Theory and Applications pages: 614-632 First Published online: October 26, 2021 First published: 10 May 2022 https://doi.org/10.1002/cta.3318AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat In Section 2.3, the second and third paragraphs should read as follows: In Equation (5), yk represents the measured value of the terminal voltage and yk* represents the estimated value of the terminal voltage. In the fuzzy adaptive system, under ideal conditions, the system innovation is zero; when the innovation and its rate of change are large, the output value will increase, and thereby, the Kalman gain is increased.1 When the innovation and its rate of change are small, the output value decreases, making the Kalman gain smaller and increasing the proportion of the measured value, to achieve the purpose of making the algorithm stable and converging quickly. The theoretical residual covariance matrix expression after introducing the adjustment factor is shown in Equation 6. P zz , k ∣ k − 1 = 1 2 n ∑ i = 1 2 n z i , k ∣ k − 1 z i , k ∣ k − 1 T − z ̂ i , k ∣ k − 1 z ̂ i , k ∣ k − 1 T + R α $$ {P}_{zz,k\mid k-1}=\frac{1}{2n}\sum \limits_{i=1}^{2n}\left({z}_{i,k\mid k-1}{z}_{i,k\mid k-1}^T-{\hat{z}}_{i,k\mid k-1}{\hat{z}}_{i,k\mid k-1}^T\right)+\frac{R}{\alpha } $$ (6) It is assumed that the innovation interval is [0, 0.04], and divide it into six segments: very small, small, medium, big, and very big. The domain of innovation change rate is set as [0, 0.035], which is divided as small, medium, big, and very big. The membership functions of ek and Δek are shown in Figure 4. The Kalman gain, innovation interval, and innovation change rate have been corrected accordingly. REFERENCE 1Yang X, Wang S, Xu W, Qiao J, Yu C, Fernandez C. Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium-ion battery state-of-charge estimation. Int J Circ Theor Appl. 2022; 50(2): 614- 632. doi:10.1002/cta.3166Wiley Online LibraryWeb of Science®Google Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation

Noise removal for semi-airborne data using wavelet threshold and singular value decomposition

Field data from the semi-airborne transient electromagnetic method (SATEM) contain various noises. Therefore, performing denoising before data inversion and interpretation is important. Currently, different types of noise are suppressed separately, not considering the variance in the signal-to-noise ratio at different time ranges. The signal details after denoising are lost and the residual noise is large. We propose a denoising scheme that uses wavelet thresholding (WT) and adaptive singular-value decomposition (SVD) filtering. We first intercepted the decay curves into several segments according to the statistical characteristics of the noise. Subsequently, a WT denoising method was used to remove the Gaussian noise and suppress noise in different signal segments. Finally, the denoised datasets were decomposed and filtered using the SVD method. The number of singular values was determined based on the optimal difference spectrum peak value, which was used to adaptively restore the responses in each segment. The proposed denoising scheme can effectively suppress multi-source noise and provide solid datasets for inversion, which was confirmed through a field survey in Shanxi Province.

Cross-free in both lateral and axial directions for Fourier-domain full-field optical coherence tomography

Fourier-domain full-field optical coherence tomography has the advantages of high resolution and parallel detection. However, using parallel detection can result in optical crosstalk. Toward minimizing crosstalk, we implemented a very fast deformable membrane (DM) that introduces random phase illumination, which can effectively reduce the crosstalk by washing out fringes originating from multiply scattered light. However, although the application of DM has reduced the crosstalk problem in parallel detection to a certain extent, there will still be a lot of background noise, which may come from the circadian rhythm of the sample and multiple scattered photons. The problem could be solved by employing adaptive singular value decomposition (SVD) filtering. We also combined SVD with the cumulative sum method, which can significanlty improve image resolution. Also, the random phase introduced by DM in the spectral domain will cause axial crosstalk after an inverse Fourier transform. As far as we know, we are the first team to notice axial crosstalk and propose that this problem can be solved by controlling the deformation range of DM. We have carried out a theoretical analysis of the above methods and verified its feasibility by simulation.

A fast adaptive SVDIncExtreme learning machine framework for remote sensing image classification

ABSTRACT Quick and efficient classification of images is important in many Remote Sensing Image (RSI) understanding tasks. The enormous availability of the RSI makes the classification a challenging task, which further needs to design more efficient models. Realizing the wide range of applications of the research area, a new fast Remote Sensing Image Classification (RSIC) framework using an Adaptive Singular Value Decomposition incorporated Extreme Learning Machine (ASVDIncELM) is proposed in this work. The classification framework uses Multi-Scale, Multi-Band (MSMB) Gabor filters to extract Opponent Colour Texture (OCT) features to represent the knowledge patterns. As the input knowledge patterns are High Dimensional (HD), an Adaptive Growing-Pruning Hidden Node Selection (AGPHNS) criterion is integrated with ASVDIncELM. A user-defined heuristic is used to select the optimal number of hidden nodes. The ASVDIncELM framework supports online learning of samples that come as blocks of fixed length. The efficiency of the framework is evaluated using three benchmark RSI datasets- UC Merced (UCM), RS-19, and PatternNet. The results obtained are promising for both offline and online scenarios in terms of Overall Accuracy (OA), Weighted Average (WA) of Precision, Recall, F1-Score, and training time.

An Enhanced Optimal Technique For Accurate Detection Of Color Face Images With Different Illuminations

Secured authentication system is one of the most challenging tasks focused now-a-days by many researchers and greatly achieved by means of face detection technique. Face image recognition is currently realized by Adaptive singular value decomposition in two-dimensional discrete Fourier domain (ASVDF). The recognition systems ability enhancement is attained for face images recognition by side light influence reduction on a color face image for inadequate light. The prevailing researches does not focuses the following points: No correct output during face recognition process, Face spoofing is not concentrated thereby face recognition may effect in imprecise result, Optimal feature extraction. Optimized Face Recognition System with Illumination and Rotation Consideration (OFRS-IRC) is one of the promising solutions for mitigating all those issues. Various methods are presented for ensuring accurate face recognition. Additive White Gaussian Noise removal technique is utilized for eliminating noise when the image is captured through sensor devices. Illuminate invariant features and locality preserving projection approach is exploited for segmented image recognition. As a final step, Fuzzy neural network is deployed for precise prediction on the basis of locality preserving projection approach results. MATLAB simulation tool is exploited for evaluating this research, where improved performance are attained by proposed method than prevailing methods. The proposed method shows 7.42% better detection rate than the existing work.

An adaptive damage detection method based on differential evolutionary algorithm for beam structures

An adaptive singular value decomposition (SVD) method is proposed to detect damage based on the differential evolutionary algorithm for beam structures. B-spline wavelet on the interval finite element method is applied to model the damaged structures. The modal shape data obtained by modal analysis are used to construct attractor trajectory matrix, and SVD-based method is adopted to detect damage locations. In order to accommodate to different damage cases, differential evolutionary algorithm (DE) is used to adaptively optimize the parameters of SVD-based method. Both simulation and experiment researches are set up to verify the proposed method. The results indicate that the presented method is accurately effective to identify the damage for beam structures.

Partial Discharge Signal Extraction Method Based on EDSSV and Low Rank RBF Neural Network

The detection process of partial discharge (PD) ultra-high frequency (UHF) signal is easily affected by white noise and periodic narrowband noise, which hinder the fault diagnosis of high-voltage electrical appliances. In order to extract PD UHF signal and suppress noise effectively, this paper provides a new method to detect PD UHF signal by EDSSV and low rank RBF neural network. Firstly, the singular value decomposition (SVD) is performed on the mixed noises of PD signal. Secondly, the peak index of energy difference spectrum of singular value (EDSSV) is selected as optimal singular value threshold, and then the periodic narrowband noise is eliminated by reconstructing the effective rank order. Finally, radial basis function (RBF) neural network is used to approximate the denoised PD signal, and Gaussian window filter is used to extract the PD signal. To verify the performance of the proposed method, we compared it with other three algorithms in simulation and field detection, including adaptive singular value decomposition (ASVD), singular value decomposition based on S-transform and MTFM (S-SVD) and EMD-WT algorithms. Particularly, four evaluation indices are designed for the detection data, which consider both the noise suppression and feature preservation. The results demonstrate the validity of the proposed method with higher signal-to-noise ratio and less waveform distortion.