Grid-free compressive mode extraction

2019 ◽  
Vol 145 (3) ◽  
pp. 1427-1442 ◽  
Author(s):  
Yongsung Park ◽  
Peter Gerstoft ◽  
Woojae Seong
Keyword(s):  
Mode Extraction ◽  
Compressive Mode

A new fault diagnosis method based on adaptive spectrum mode extraction

2021 ◽  
pp. 147592172098694
Author(s):  
Zhijian Wang ◽  
Ningning Yang ◽  
Naipeng Li ◽  
Wenhua Du ◽  
Junyuan Wang
Keyword(s):  
Scale Space ◽  
Variational Mode Decomposition ◽  
Rolling Bearings ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Diagnosis Method ◽  
Mode Extraction ◽  
Non Stationary Signal ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

Variational mode decomposition provides a feasible method for non-stationary signal analysis, but the method is still not adaptive, which greatly limits the wide application of the method. Therefore, an adaptive spectrum mode extraction method is proposed in this article. The proposed method is mainly composed of spectral segmentation, mode extraction, and feedback adjustment. In the spectral segmentation part, considering the lack of robustness of classical scale space in a strong noise environment, this article proposes a method of fault feature mapping, which solves over-decomposition of variational mode decomposition guided by classical scale space. In the mode extraction part, for insufficient self-adaptability of the single penalty factor in the variational mode decomposition method, this article proposes a method of spectral aggregation factor, which solves multiple penalty factors that conform to different intrinsic modal functions. In the feedback adjustment part, this article proposes the method of transboundary criterion, which makes the result of variational mode decomposition within a preset range. Finally, using dispersion entropy and kurtosis indicators, intrinsic modal function components containing fault frequencies are extracted for envelope spectrum analysis to extract fault characteristic frequencies. In order to verify the effectiveness of the proposed method, the proposed method is applied to simulation signals and bearing fault signals. Comparing the decomposition results of different methods, the conclusion shows that the proposed method is more advantageous for the fault feature extraction of rolling bearings.

An optimized variational mode extraction method for rolling bearing fault diagnosis

2021 ◽  
pp. 147592172110066
Author(s):  
Bin Pang ◽  
Mojtaba Nazari ◽  
Zhenduo Sun ◽  
Jiaying Li ◽  
Guiji Tang
Keyword(s):  
Extraction Method ◽  
Fitness Function ◽  
Rolling Bearing ◽  
Initial Guess ◽  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Extraction

The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault feature extraction. Variational mode extraction is a novel signal analysis method designed to retrieve a specific signal component from the composite signal. Variational mode extraction is founded on a similar basis as variational mode decomposition, while it shows better accuracy and higher efficiency compared with variational mode decomposition. In this study, variational mode extraction is introduced to the resonance demodulation analysis of bearing fault. As the results of variational mode extraction analysis are greatly influenced by the choice of two parameters, that is, the balancing factor α and the initial guess of center frequency ωd, an optimized variational mode extraction method is further developed. First, a new fault information evaluation index for measuring the richness of fault characteristics of the signal, termed ensemble impulsiveness and cyclostationarity, is formulated. Second, the ensemble impulsiveness and cyclostationarity is used as the fitness function of particle swarm optimization to automatically determine the optimal values of α and ωd. Finally, the validity of optimized variational mode extraction method is verified by simulated and experimental analysis, and the superiority of optimized variational mode extraction method is highlighted through comparison with two other advanced resonance demodulation analysis approaches, that is, the improved kurtogram and infogram. The analysis results indicate that optimized variational mode extraction method has a powerful capability of resonance demodulation analysis.

Radar target discrimination by convolution of radar return with extinction-pulses and single-mode extraction signals

1986 ◽  
Vol 34 (7) ◽  
pp. 896-904 ◽  
Author(s):  
Kun-Mu Chen ◽  
D. Nyquist ◽  
E. Rothwell ◽  
L. Webb ◽  
B. Drachman
Keyword(s):  
Single Mode ◽  
Radar Target ◽  
Target Discrimination ◽  
Mode Extraction

scholarly journals Depth Discrimination for Low-Frequency Sources Using a Horizontal Line Array of Acoustic Vector Sensors Based on Mode Extraction

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3692 ◽  
Author(s):  
Guolong Liang ◽  
Yifeng Zhang ◽  
Guangpu Zhang ◽  
Jia Feng ◽  
Ce Zheng
Keyword(s):  
Sound Speed ◽  
Linear Equations ◽  
Robustness Analysis ◽  
Low Frequency ◽  
Horizontal Line ◽  
Testing Hypotheses ◽  
Depth Discrimination ◽  
Line Array ◽  
Vector Sensors ◽  
Mode Extraction

Depth discrimination is a key procedure in acoustic detection or target classification for low-frequency underwater sources. Conventional depth-discrimination methods use a vertical line array, which has disadvantage of poor mobility due to the size of the sensor array. In this paper, we propose a depth-discrimination method for low-frequency sources using a horizontal line array (HLA) of acoustic vector sensors based on mode extraction. First, we establish linear equations related to the modal amplitudes based on modal beamforming in the vector mode space. Second, we solve the linear equations by introducing the total least square algorithm and estimate modal amplitudes. Third, we select the power percentage of the low-order modes as the decision metric and construct testing hypotheses based on the modal amplitude estimation. Compared with a scalar sensor, a vector sensor improves the depth discrimination, because the mode weights are more appropriate for doing so. The presented linear equations and the solution algorithm allow the method to maintain good performance even using a relatively short HLA. The constructed testing hypotheses are highly robust against mismatched environments. Note that the method is not appropriate for the winter typical sound speed waveguide, because the characteristics of the modes differ from those in downward-refracting sound speed waveguide. Robustness analysis and simulation results validate the effectiveness of the proposed method.

scholarly journals Research on a Rail Defect Location Method Based on a Single Mode Extraction Algorithm

2019 ◽  
Vol 9 (6) ◽  
pp. 1107 ◽  
Author(s):  
Bo Xing ◽  
Zujun Yu ◽  
Xining Xu ◽  
Liqiang Zhu ◽  
Hongmei Shi
Keyword(s):  
Finite Element ◽  
Guided Waves ◽  
Single Mode ◽  
Dispersion Curves ◽  
Modal Identification ◽  
Propagation Time ◽  
Location Method ◽  
Defect Location ◽  
Extraction Algorithm ◽  
Mode Extraction

This paper proposes a rail defect location method based on a single mode extraction algorithm (SMEA) of ultrasonic guided waves. Simulation analysis and verification were conducted. The dispersion curves of a CHN60 rail were obtained using the semi-analytical finite element method, and the modal data of the guided waves were determined. According to the inverse transformation of the excitation response algorithm, modal identification under low-frequency and high-frequency excitation was realized, and the vibration displacements at other positions of a rail were successfully predicted. Furthermore, an SMEA for guided waves is proposed, through which the single extraction results of four modes were successfully obtained when the rail was excited along different excitation directions at a frequency of 200 Hz. In addition, the SMEA was applied to defect location detection, and the single reflection mode waveform of the defect was extracted. Based on the group velocity of the mode and its propagation time, the distance between the defect and the excitation point was measured, and the defect location was predicted as a result. Moreover, the SMEA was applied to locate the railhead defect. The detection mode, the frequency, and the excitation method Were selected through the dispersion curves and modal identification results, and a series of signals of the sampling nodes were obtained using the three-dimensional finite element software ANSYS. The distance between the defect and the excitation point was calculated using the SMEA result. When compared with the structure of the simulated model, the errors obtained were all less than 0.5 m, proving the efficacy of this method in precisely locating rail defects, thus providing an innovated solution for rail defect location.

Identification of aerodynamic derivatives of bridge decks at high testing wind speed

2018 ◽  
Vol 45 (11) ◽  
pp. 1004-1014
Author(s):  
Quanshun Ding ◽  
Shuanghu Dong ◽  
Zhiyong Zhou
Keyword(s):  
Wind Speed ◽  
Wind Tunnel Test ◽  
Participation Rate ◽  
Single Mode ◽  
High Wind ◽  
Wind Speeds ◽  
Aerodynamic Derivatives ◽  
Vibration Responses ◽  
Mode Extraction ◽  
Derivatives Of

An identification of eight aerodynamic derivatives based on dual-mode and single-mode extraction of system is presented to improve the applicability and accuracy of identification at high testing wind speed. The participation rate to measure the contribution of modes on free-vibration responses is defined and the single-mode extraction is presented to extract the modal parameters of the system at high wind speed. To verify the reliability and applicability of the presented method, the aerodynamic derivatives of a dummy section with known self-excited forces are identified. It is noted that there is a very good agreement between the identified results and the target ones in the range of the low and high wind speeds and the presented method works well after the critical state of flutter. The sectional wind tunnel test of the Tanggu-haihe bridge is performed to identify the aerodynamic derivatives of the deck at the attack angles of −3°, 0°, and 3°.

Mode extraction from acoustic backscatter in a waveguide

2005 ◽  
Vol 117 (4) ◽  
pp. 2612-2612
Author(s):  
Shane Walker ◽  
Philippe Roux ◽  
William A. Kuperman
Keyword(s):  
Acoustic Backscatter ◽  
Mode Extraction
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