Dispersive MUSIC algorithm for Lamb wave phased array

2022 ◽  
Author(s):  
Caibin Xu ◽  
Hao Zuo ◽  
Mingxi Deng
Keyword(s):  
Lamb Wave ◽  
Phased Array ◽  
Wave Packets ◽  
Processing Technique ◽  
Post Processing ◽  
Music Algorithm ◽  
Time Saving ◽  
Baseline Subtraction ◽  
Wave Imaging ◽  
Virtual Array

Abstract By controlling the excitation time delay on each element, the conventional phased array can physically focus signals transmitted by different elements on a desired point in turn. An alternative and time-saving strategy is that every element takes turns to transmit the excitation and the remaining elements receive the corresponding response signals, which is known as the full matrix capture (FMC) method for data acquisition, and then let the signals virtually focus on every desired point by post-processing technique. In this study, based on the FMC, a dispersive multiple signal classification (MUSIC) algorithm for Lamb wave phased array is developed to locate defects. The virtual time reversal is implemented to back propagate the wave packets corresponding to the desired focusing point and a window function is adopted to adaptively isolate the desired packets from the other components. Then those wave packets are forward propagated to the original focusing point at a constant velocity. For every potential focusing point and all receivers, the virtual array focuses the signals from all transmitters so as to obtain the focusing signals. The MUSIC algorithm with the obtained focusing signals is adopted to achieve Lamb wave imaging. Benefiting from the post-processing operations, the baseline subtraction as well as the estimation for the number of the scattering sources is no longer required in the proposed algorithm. Experiments on an aluminum plate with three artificial defects and a compact circular PZT array are implemented and the results demonstrate the efficacy of the proposed algorithm.

Damage localization with fiber Bragg grating Lamb wave sensing through adaptive phased array imaging

2018 ◽  
Vol 18 (1) ◽  
pp. 334-344 ◽  
Author(s):  
Zhenhua Tian ◽  
Lingyu Yu ◽  
Xiaoyi Sun ◽  
Bin Lin
Keyword(s):  
Fiber Bragg Grating ◽  
Electromagnetic Interference ◽  
Lamb Wave ◽  
Phased Array ◽  
Minimum Variance ◽  
Bragg Grating ◽  
Damage Localization ◽  
Weighting Factors ◽  
Wave Imaging ◽  
Grating Array

Fiber Bragg gratings are known being immune to electromagnetic interference and emerging as Lamb wave sensors for structural health monitoring of plate-like structures. However, their application for damage localization in large areas has been limited by their direction-dependent sensor factor. This article addresses such a challenge and presents a robust damage localization method for fiber Bragg grating Lamb wave sensing through the implementation of adaptive phased array algorithms. A compact linear fiber Bragg grating phased array is configured by uniformly distributing the fiber Bragg grating sensors along a straight line and axially in parallel to each other. The Lamb wave imaging is then performed by phased array algorithms without weighting factors (conventional delay-and-sum) and with adaptive weighting factors (minimum variance). The properties of both imaging algorithms, as well as the effects of fiber Bragg grating’s direction-dependent sensor factor, are characterized, analyzed, and compared in details. The results show that this compact fiber Bragg grating array can precisely locate damage in plates, while the comparisons show that the minimum variance method has a better imaging resolution than that of the delay-and-sum method and is barely affected by fiber Bragg grating’s direction-dependent sensor factor. Laboratory tests are also performed with a four–fiber Bragg grating array to detect simulated defects at different directions. Both delay-and-sum and minimum variance methods can successfully locate defects at different positions, and their results are consistent with analytical predictions.

scholarly journals Plane Wave Imaging through Interfaces

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4967
Author(s):  
Guillermo Cosarinsky ◽  
Jorge F. Cruza ◽  
Jorge Camacho
Keyword(s):  
Image Quality ◽  
Plane Wave ◽  
Phased Array ◽  
Mathematical Formulation ◽  
Non Destructive Testing ◽  
Arbitrary Geometry ◽  
Destructive Testing ◽  
Wave Imaging ◽  
Imaging Strategy ◽  
Non Destructive

Plane Wave Imaging (PWI) has been recently proposed for fast ultrasound inspections in the Non-Destructive-Testing (NDT) field. By using a single (or a reduced number) of plane wave emissions and parallel beamforming in reception, frame rates of hundreds to thousands of images per second can be achieved without significant image quality losses with regard to the Total Focusing Method (TFM) or Phased Array (PA). This work addresses the problem of applying PWI in the presence of arbitrarily shaped interfaces, which is a common problem in NDT. First, the mathematical formulation for generating a plane wave inside a component of arbitrary geometry is given, and the characteristics of the resultant acoustic field are analyzed by simulation, showing plane wavefronts with non-uniform amplitude. Then, an imaging strategy is proposed, accounting for this amplitude effect. Finally, the proposed method is experimentally validated, and its application limits are discussed.

scholarly journals A post-processing technique for stabilizing the discontinuous pressure projection operator in marginally-resolved incompressible inviscid flow

2016 ◽  
Vol 139 ◽  
pp. 120-129 ◽  
Author(s):  
Sumedh M. Joshi ◽  
Peter J. Diamessis ◽  
Derek T. Steinmoeller ◽  
Marek Stastna ◽  
Greg N. Thomsen
Keyword(s):  
Projection Operator ◽  
Inviscid Flow ◽  
Processing Technique ◽  
Post Processing ◽  
Pressure Projection ◽  
Discontinuous Pressure

scholarly journals Video capture and post-processing technique for approximating 3D projectile trajectory

2015 ◽  
Vol 8 (3-4) ◽  
pp. 124-129 ◽  
Author(s):  
Chase M. Pfeifer ◽  
Judith M. Burnfield ◽  
Guilherme M. Cesar ◽  
Max H. Twedt ◽  
Jeff A. Hawks
Keyword(s):  
Processing Technique ◽  
Video Capture ◽  
Post Processing

scholarly journals ScoreNet: A neural network-based post-processing model for identifying epileptic seizure onset and offset in EEGs

2020 ◽  
Author(s):  
Poomipat Boonyakitanont ◽  
Apiwat Lek-uthai ◽  
Jitkomut Songsiri
Keyword(s):  
Neural Network ◽  
Epileptic Seizure ◽  
False Positive Rate ◽  
Imbalanced Data ◽  
Detection Algorithm ◽  
Processing Technique ◽  
Post Processing ◽  
Scalp Eeg ◽  
Positive Rate ◽  
Eeg Recordings

AbstractThis article aims to design an automatic detection algorithm of epileptic seizure onsets and offsets in scalp EEGs. A proposed scheme consists of two sequential steps: the detection of seizure episodes, and the determination of seizure onsets and offsets in long EEG recordings. We introduce a neural network-based model called ScoreNet as a post-processing technique to determine the seizure onsets and offsets in EEGs. A cost function called a log-dice loss that has an analogous meaning to F1 is proposed to handle an imbalanced data problem. In combination with several classifiers including random forest, CNN, and logistic regression, the ScoreNet is then verified on the CHB-MIT Scalp EEG database. As a result, in seizure detection, the ScoreNet can significantly improve F1 to 70.15% and can considerably reduce false positive rate per hour to 0.05 on average. In addition, we propose detection delay metric, an effective latency index as a summation of the exponential of delays, that includes undetected events into account. The index can provide a better insight into onset and offset detection than conventional time-based metrics.

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