Time-varying inverse filtering of narrowband ultrasonic signals

Moll, Jochen; Heftrich, Christopher; Fritzen, Claus‐Peter

doi:10.1177/1475921710379520

Cited by 20 publications

(7 citation statements)

References 31 publications

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“…By means of a suitable dictionary it is possible to apply the method to fiber-reinforced structures, too. In the framework of SHM, deconvolution of guided wave signals have been demonstrated in [17,18]. In the context of ultrasound non-destructive testing (NDT) several sparse deconvolution strategies have been proposed [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Deconvolution processing for improved acoustic wavefield imaging

Kexel

Moll

2014

Case Studies in Nondestructive Testing and Evaluation

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Sparse sensor networks for Lamb wave-based structural health monitoring (SHM) can detect defects in plate-like structures. However, the limited number of sensor positions provides little information to characterize the unknown scatterer. This can be achieved by full wavefield analysis e.g. using Laser Doppler vibrometry measurements. This paper proposes deconvolution processing that enhances the acoustic wavefield interpretation by increasing the temporal resolution of the underlying ultrasound signals. Applying this preprocessor to the whole wavefield allows improved non-destructive assessment of the defect. This approach is verified experimentally through a case study on an isotropic aluminum plate with four cracks.

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Section: Introductionmentioning

confidence: 99%

Deconvolution processing for improved acoustic wavefield imaging

Kexel

Moll

2014

Case Studies in Nondestructive Testing and Evaluation

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“…However, more PZTs and higher-performance equipment are required, adding the experiment cost. Besides, some filtering methods are applied to improve the SNR, such as a Fourier filter [29,30], a Morlet wavelet filter [21,31], an adaptive filter [32], a comb filter [33], a Kalman filter [34][35][36], an extended Kalman filter [37,38], a particle filter [39][40][41], a time-varying inverse filter [42], a polynomial-smoothing filter [43,44], and an optimally matched filter [45]. These methods can weaken the noise signal to a certain extent, but the effective signals are simultaneously weakened.…”

Section: Introductionmentioning

confidence: 99%

Structural-damage localization using ultrasonic guided waves based on the lossless filtering method

Zheng

Zhao

Tian

et al. 2020

Smart Mater. Struct.

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Ultrasonic guided waves have been widely used for structural-damage localization in recent decades, whose effectiveness depends on whether the damage on a structure reflects a wave pulse. The basic principle can be described as follows: the ratio between the times of flight (TOFs) of the excitation and damage signals is equal to that between their distances of flight. Because a number of disturbing factors occurred during the data-acquisition process, a large number of noise signals present in the experiment could cause a low signal-to-noise ratio (SNR). In addition, whereas the traditional filtering methods improved the SNR, the effective signals were weakened. To deal with this situation, an ultrasonic lossless filtering method based on the multi-coverage technique applied to a blasting seismic wave and the superposition technique to ultrasonic guided wave was first proposed to enhance the SNR. Compared with the traditional filtering techniques, the proposed method achieves better performance in that the effective signal remains unaltered and the noise level is reduced. The Wigner-Ville distribution (WVD) method, which could reflect the true energy distribution within the time and frequency domains, was applied to calculate the TOF of the signals. Finally, the damage localization of the structure was realized according to the abovementioned principle. Compared with the location result without a filter, the maximum WVD value becomes more perceivable, and the positioning accuracy is clearly improved. Compared with the location results using the Fourier and polynomial-smoothing filtering methods, the lossless filtering method allows an effective signal to remain unaltered. Finally, we can conclude that the proposed ultrasonic lossless filtering method can improve the SNR under the condition in which the effective signal remains unaltered, and the SNRs become higher and higher with the increase of superimposition times. The combination of ultrasonic lossless filtering method and the WVD method can perfectly realize the damage location with a high accuracy. Several damage-localization experiments demonstrate that the ultrasonic lossless filtering method is reliable.

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“…MP is a greedy algorithm that finds the best match for a signal in from an over-complete and redundant dictionary. This method has already been reported for guided wave inspection and defect characterization [ 27 , 28 , 29 , 30 , 31 , 32 ]. However, the majority of the efforts in MP for guided wave inspection have neglected the dispersive nature of guided waves.…”

Section: Introductionmentioning

confidence: 99%

“…However, the majority of the efforts in MP for guided wave inspection have neglected the dispersive nature of guided waves. Some scholars have reported utilizing Chirplet Matching Pursuit and tried to estimate the dispersion by chirp functions [ 31 , 33 , 34 ]. Despite being effective, these methods suffer from mathematical complications.…”

Section: Introductionmentioning

confidence: 99%

Sparse and Dispersion-Based Matching Pursuit for Minimizing the Dispersion Effect Occurring when Using Guided Wave for Pipe Inspection

2017

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Ultrasonic guided wave is an effective tool for structural health monitoring of structures for detecting defects. In practice, guided wave signals are dispersive and contain multiple modes and noise. In the presence of overlapped wave-packets/modes and noise together with dispersion, extracting meaningful information from these signals is a challenging task. Handling such challenge requires an advanced signal processing tool. The aim of this study is to develop an effective and robust signal processing tool to deal with the complexity of guided wave signals for non-destructive testing (NDT) purpose. To achieve this goal, Sparse Representation with Dispersion Based Matching Pursuit (SDMP) is proposed. Addressing the three abovementioned facts that complicate signal interpretation, SDMP separates overlapped modes and demonstrates good performance against noise with maximum sparsity. With the dispersion taken into account, an overc-omplete and redundant dictionary of basic atoms based on a narrowband excitation signal is designed. As Finite Element Method (FEM) was used to predict the form of wave packets propagating along structures, these atoms have the maximum resemblance with real guided wave signals. SDMP operates in two stages. In the first stage, similar to Matching Pursuit (MP), the approximation improves by adding, a single atom to the solution set at each iteration. However, atom selection criterion of SDMP utilizes the time localization of guided wave reflections that makes a portion of overlapped wave-packets to be composed mainly of a single echo. In the second stage of the algorithm, the selected atoms that have frequency inconsistency with the excitation signal are discarded. This increases the sparsity of the final representation. Meanwhile, leading to accurate approximation, as discarded atoms are not representing guided wave reflections, it simplifies extracting physical meanings for defect detection purpose. To verify the effectiveness of SDMP for damage detection results from numerical simulations and experiments on steel pipes are presented.

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Time-varying inverse filtering of narrowband ultrasonic signals

Cited by 20 publications

References 31 publications

Deconvolution processing for improved acoustic wavefield imaging

Deconvolution processing for improved acoustic wavefield imaging

Structural-damage localization using ultrasonic guided waves based on the lossless filtering method

Sparse and Dispersion-Based Matching Pursuit for Minimizing the Dispersion Effect Occurring when Using Guided Wave for Pipe Inspection

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