Forecast pipe defect size based on modified grey system and guided wave signal recognition with matching pursuit

2012 ◽  
Author(s):  
Chuanjun Shen ◽  
Yuemin Wang ◽  
Yan Liu ◽  
Fangjun Zhou ◽  
Fengrui Sun
Keyword(s):  
Matching Pursuit ◽  
Guided Wave ◽  
Defect Size ◽  
Signal Recognition ◽  
Grey System ◽  
Wave Signal

The method of bolt axial force looseness monitoring and control by piezoelectric ceramics in bolted joint structures

2021 ◽  
pp. 095440622110398
Author(s):  
Xinyao Sun ◽  
Jinggan Shao ◽  
Yang Zhou ◽  
Ci Yuan ◽  
Yang Li ◽  
...  
Keyword(s):  
Finite Element ◽  
Active Control ◽  
Axial Force ◽  
Piezoelectric Ceramic ◽  
Control Method ◽  
Piezoelectric Effect ◽  
Guided Wave ◽  
Clamping Force ◽  
Monitoring And Control ◽  
Wave Signal

Aiming at the problem of bolt looseness in structures, this paper proposes an active control method of axial force monitoring through guided wave and axial force compensation via the inverse piezoelectric effect of a piezoelectric ceramic gasket. Based on the finite element model, the propagation process of guided wave wave in bolted connectors is analyzed, which shows that the transmitted wave energy increases with the increase of bolt clamping force. The analysis of the stress-strain characteristics of the axially polarized and radially polarized piezoelectric ceramic gasket shows that the axially polarized piezoelectric ceramic gasket is more suitable for the control of bolt clamping force. The finite element analysis of the application of piezoelectric ceramic gasket in bolt axial force control shows that the power of guided wave signal increases monotonously with the increase of loaded electric field strength. In accordance with these theoretical methods and research, an active control system for bolt axial force is established in this experiment. The system monitors the power of the guided wave signal in real time and controls the axial force of the bolt by adjusting the intensity of the piezoelectric effect, which achieves an accurate control effect.

Model Classification of Guided Wave Signal based on the Visibility Graph and SVM

2018 ◽  
Author(s):  
Weilei MU ◽  
Zhengxing ZOU ◽  
Hailiang SUN ◽  
Guijie LIU ◽  
Guangyin XIA ◽  
...  
Keyword(s):  
Guided Wave ◽  
Visibility Graph ◽  
Wave Signal ◽  
Model Classification

scholarly journals A Low-Complexity Compressed Sensing Reconstruction Method for Heart Signal Biometric Recognition

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5330
Author(s):  
Xiao ◽  
Hu ◽  
Shao ◽  
Li
Keyword(s):  
Compressed Sensing ◽  
Matching Pursuit ◽  
Low Complexity ◽  
Portable Devices ◽  
Reconstruction Method ◽  
Signal Recognition ◽  
Bioelectric Signal ◽  
Increasing Demand ◽  
Feature Values ◽  
Compression Sensing

Biometric systems allow recognition and verification of an individual through his or her physiological or behavioral characteristics. It is a growing field of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on compression sensing was studied using bioelectric signals, which aimed to increase the limited resources of portable remote bioelectric signal recognition equipment. Using electrocardiograms (ECGs) and photoplethysmograms (PPGs) of heart signals as research data, an improved segmented weak orthogonal matching pursuit (OMP) algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis. The accuracy of the proposed method and the practicability of compression sensing in cardiac signal identification were verified. Experiments showed that the reconstructed ECG and PPG signal recognition rates were 95.65% and 91.31%, respectively, and that the residual value was less than 0.05 mV, which indicates that the proposed method can be effectively used for two bioelectric signal compression reconstructions.

scholarly journals Study on the Property of Guided Wave Signal Analysis according to Defect Shape of Small Size

2012 ◽  
Vol 32 (4) ◽  
pp. 410-417
Author(s):  
Doo-Song Gil ◽  
Yeon-Shik Ahn ◽  
Gye-Jo Jung ◽  
Sang-Gi Park ◽  
Yong-Gun Kim
Keyword(s):  
Signal Analysis ◽  
Guided Wave ◽  
Wave Signal

A Simplified Dispersion Compensation Algorithm for the Interpretation of Guided Wave Signals

2019 ◽  
Vol 141 (2) ◽  
Author(s):  
Wenjun Wu ◽  
Yuemin Wang
Keyword(s):  
Guided Waves ◽  
Matching Pursuit ◽  
Dispersion Compensation ◽  
Wave Dispersion ◽  
Guided Wave ◽  
Center Frequency ◽  
Practical Applications ◽  
Compensation Algorithm ◽  
Nonlinear Phase ◽  
Mode Separation

Due to the multimodal and dispersive characteristics of guided waves, guided wave testing signals are always overlapped and difficult to separate for correct interpretations. To this end, a simplified dispersion compensation algorithm is put forward in this paper. The dispersion elimination is accomplished by compensating the second-order nonlinear phase shift of guided wave signals, which is the cause of the dispersion when narrow band exciting signals are used. This algorithm is easy to implement and has no need of prior knowledge of the guided wave dispersion relationship. Considering that the center frequency, which is a key parameter for this algorithm, is nearly impossible to determine accurately in practical applications, the effect of the center frequency deviation on the algorithm is further studied. Both theoretical analysis and numerical simulation indicate the insensitivity of the algorithm to the deviation of the center frequency, and hence, there is no need to determine the center frequency accurately, facilitating the practical use of the algorithm. Based on this simplified dispersion compensation algorithm and in cooperation with the matching pursuit method, the mode separation is further performed for interpreting of overlapped guided wave signals. Dispersion compensation is first applied to the testing signal with respect to a certain mode which will compress the waveform of the mode while the others still spread. Then, this compressed waveform is separated with the Gabor based matching pursuit method. Both simulation and experiment are designed to demonstrate the effectiveness of the proposed methods.

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