Damage localization in aluminum plate with compact rectangular phased piezoelectric transducer array

Liu, Zenghua; Sun, Kunming; Song, Guanyu; He, Cunfu; Wu, Bin

doi:10.1016/j.ymssp.2015.09.022

Cited by 69 publications

(34 citation statements)

References 39 publications

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“…5 These phase-based algorithms are not sensitive to the dispersion nature of Lamb waves. 6 Some researchers synthesized different imaging algorithms to obtain defect imaging results with high resolution, such as combining the TFM and SCF, [7][8][9] and the TFM and multi-apodization polarity technique. 10 The elliptic imaging algorithm 11,12 and hyperbolic imaging algorithm 13 are two typical algorithms based on sparse arrays where the distance between adjacent transducers is larger than the largest wavelength of waves.…”

Section: Introductionmentioning

confidence: 99%

An intelligent algorithm based on evolutionary strategy and clustering algorithm for Lamb wave defect location

Chen

Liu

et al. 2020

Structural Health Monitoring

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Imaging algorithms for visualization of defects play a significant role in Lamb wave–based research of nondestructive testing and structural health monitoring. In classical algorithms, the position or distribution of defects is located by mapping the amplitude or phase information of signals from the time domain to every discrete spatial grid of the structure. It is time-consuming. In this study, the diversity, statistical, and fuzzy characteristics of the elliptic imaging algorithm are analyzed first; then, an intelligent defect location algorithm is proposed based on the evolutionary strategy and the K-means algorithm. The position of defects can be identified by observing the distribution of individuals. There are six parts in the proposed algorithm, including the data structure design, adaptive population screening, adaptive population reproduction, diversity maintenance mechanism, and cutoff criterion. Considering the statistical and fuzzy characteristics in the detection, several specific input parameters are defined in our algorithm, such as the distance-dependent screening threshold, path-dependent residual vector, and path-independent residual. To maintain the diversity of individuals in the analysis, we have made two adjustments to the evolutionary strategy: one is to optimize the population screening and reproduction steps with the K-means algorithm, and the other is to add a diversity maintenance method into the evolutionary strategy. The effectiveness of the proposed intelligent defect location algorithm is verified by numerical simulations and experiments. Numerical studies indicate that the proposed algorithm has a reliable performance in the detection of defects with different shapes and sizes. In the experimental research, we demonstrate that the efficiency of the proposed algorithm is about 200 times faster than the elliptic imaging algorithm. And the optimum parameter setting of the algorithm is investigated by analyzing the influence of parameter setting on the detection.

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Section: Introductionmentioning

confidence: 99%

An intelligent algorithm based on evolutionary strategy and clustering algorithm for Lamb wave defect location

Chen

Liu

et al. 2020

Structural Health Monitoring

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“…It is known that ultrasonic transducer arrays are able to provide increased flexibility and superior performance. Therefore, Liu et al (2016) localized the damage in plate-like structures by analysis conducted on the signals recorded by transducer array. In addition, Yan and Rose (2007) applied the linear phased piezoelectric transducer array to composite aircraft panels, which accordingly suppressed the influence of anisotropy by employing a modified delay-and-sum algorithm.…”

Section: Introductionmentioning

confidence: 99%

Damage imaging for composite using Lamb wave based on minimum variance distortion-less response method

Jiang

et al. 2019

Transactions of the Institute of Measurement and Control

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It is highly probable for the structures of carbon fiber reinforced plastics (CFRP) to suffer from invisible impact damage, for which the identification of such damage is of great significance. For the purpose of damage localization, this paper proposes a minimum variance distortion-less response (MVDR) algorithm that employs linear array sensor. In addition, a near field signal model based on active Lamb wave is established and the propagation of Lamb wave is investigated in composite laminates through the method of simulation. Moreover, a damage imaging system is also set up for composites to verify the effectiveness of the MVDR algorithm. Besides, in the study, wavelet transform is used as well to extract narrowband signals and promote signal-to-noise. It is shown by the results that the proposed algorithm can accurately identify the location of damage through the method of imaging. It is concluded that the maximum error of damage identification is 1.2 cm and MVDR algorithm has great potential in the field, damage imaging of composite materials. As a result, it provides a novel idea for damage imaging of composite materials.

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“…In most applications, guided Lamb wave is generated and received by piezoelectric transducer (PZT) glued to the test specimen [6,7,8]. Many PZT sensor and Lamb wave-based damage monitoring methods have been studied, including the delay-and-sum [9,10,11], time reversal focusing–based [12,13,14], probability-based diagnostic [15,16,17], ultrasonic phased array [18,19,20], and multiple signal classification [21,22,23,24] imaging methods. In these methods, damage is identified and characterized by analyzing the characteristics of the damage monitoring signal in the time and/or frequency domain after the damage affects the Lamb wave scattering, such as time–of–flight, amplitude and energy.…”

Section: Introductionmentioning

confidence: 99%

A Lamb Wave Wavenumber-Searching Method for a Linear PZT Sensor Array

Liu

Zhao

2019

Sensors

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In this paper, a wavenumber–searching method based on time-domain compensation is proposed to obtain the wavenumber of the Lamb wave array received signal. In the proposed method, the time-domain sampling signal of the linear piezoelectric transducer (PZT) sensor array is converted into a spatial sampling signal using the searching wavenumber. The two–dimensional time-spatial-domain Lamb wave received signal of the linear PZT sensor array is then converted into a one-dimensional synthesized spatial sampling signal. Further, the sum of squared errors between the synthesized spatial sampling signal and its Morlet wavelet fitting signal is calculated at each searching wavenumber. Finally, the wavenumber of the Lamb wave array received signal is obtained as the searching wavenumber corresponding to the minimum error. This method was validated on a 2024-T3 aluminum alloy. The validation results showed that the proposed method can successfully obtain the wavenumber of the Lamb wave array received signal, whose spatial sampling rate does not satisfy the Nyquist sampling theorem; the wavenumber error does not exceed 2.2 rad/m. Damage localization based on the proposed method was also validated on a carbon fiber composite laminate plate, and the maximum damage localization error was no more than 2.11 cm.

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Damage localization in aluminum plate with compact rectangular phased piezoelectric transducer array

Cited by 69 publications

References 39 publications

An intelligent algorithm based on evolutionary strategy and clustering algorithm for Lamb wave defect location

An intelligent algorithm based on evolutionary strategy and clustering algorithm for Lamb wave defect location

Damage imaging for composite using Lamb wave based on minimum variance distortion-less response method

A Lamb Wave Wavenumber-Searching Method for a Linear PZT Sensor Array

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