Wavelet-based built-in damage detection and identification for composites

Yan, Gang; Zhou, Lily L.; Fang, Yuan

doi:10.1117/12.602435

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…Intelligent algorithms include ant colony algorithms, bee colony algorithms, and genetic algorithms 16,17 , which are used in the research of ultrasonic signal characteristic parameter analysis 18 and material property inversion in the field of NDT 19 . Yan et al 20 used a genetic algorithm and composite theoretical group velocity to analyze a defect location model based on the time difference of the flight of the scattered signals to detect the position and size of a single defect in composite plates. Chen et al 21 proposed an intelligent Lamb wave defect location algorithm based on an evolutionary strategy and a clustering algorithm, which determined the accurate location of different types of defects in aluminum plates.…”

Section: Introductionmentioning

confidence: 99%

Intelligent defect location of a U-shaped boom using helical guided waves

Liu

Jiang

et al. 2022

Structural Health Monitoring

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Truck cranes are indispensable heavy-duty loading and unloading equipment in industrial production. A boom is the key load-bearing component of a truck crane, and its health has a vital influence on a crane’s lifting performance and safety in production. Therefore, it is urgent to develop a structural health monitoring (SHM) method for a boom structure. In this research, an improved intelligent defect location algorithm based on helical guided waves is applied to the defect detection of a U-shaped boom. The improved intelligent algorithm is a defect location algorithm based on the ellipse imaging principle, which combines an evolutionary algorithm with a K-means algorithm and can identify the location of defects through the distribution of individuals. According to the propagation characteristics of the helical guided waves in the structure of a U-shaped boom, an optimization scheme for the improved intelligent defect location algorithm is proposed that fully considers the fact that the group velocity of the helical guided waves varies with the wall thickness, and the corresponding group velocity is used to accurately calculate the arrival time of each initial individual to improve the accuracy of the defect location. Numerical simulations and experiments are conducted to verify the effectiveness of the proposed improved intelligent defect location algorithm in the defect location algorithm. When only one group velocity is considered, the improved intelligent defect location algorithm is in good agreement with the elliptical imaging algorithm. When the group velocities for different wall thicknesses are fully considered, the detection results of the optimized intelligent defect location algorithm have a higher resolution. This optimization algorithm provides a tool for the SHM of a U-shaped boom based on guided waves.

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Section: Introductionmentioning

confidence: 99%

Intelligent defect location of a U-shaped boom using helical guided waves

Liu

Jiang

et al. 2022

Structural Health Monitoring

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“…It is not suitable for the detection based on the ToF of waves and the sparse arrays. Yan et al 27 used the GA to analyze Lamb wave signals and identified the location and size of defects in a composite plate. The optimization function model was generated by subtracting the direct ToF from the measured ToF of scattering signals these received by every actuator-receiver pair.…”

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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“…Yan et al proposed a wavelet-based, built-in damage detection and identification algorithm for carbon fibre reinforced polymer (CFRP) laminate. 15 Combined with the theoretically computed wave velocity, a genetic algorithm (GA) technique is employed to identify the location and size of the damage. The results show an error of around 4.5%.…”

Section: Introductionmentioning

confidence: 99%

Localization of Damages in Plain And Riveted Aluminium Specimens using Lamb Waves

Andhale

Masurkar²,

Yelve³

2019

The International Journal of Acoustics and Vibration

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The Lamb wave-based localization of damage is presented here separately for the plain and riveted aluminium (Al) specimens. The first part of this paper deals with the experimental damage localization of the plain Al specimen using Lamb waves and four piezoelectric wafer (PW) transducers. The PW transducers mounted onto the specimen in a collocated way are used to actuate and sense Lamb waves. The responses are obtained for both the pristine and damaged states of the Al specimen. The signal processing is carried out on the residual response using the continuous wavelet transform (CWT), and time of arrival (TOA) data is obtained for each collocated actuator-sensor pair. The TOA data of the wave reflected from the damage is used in the two arrival time difference and astroid algorithms to locate the damage in an enclosed area. The genetic optimization (GO) method is used to further refine the location of damage within the enclosed area obtained using astroid algorithm. The second part of the paper deals with the localization of a faulty rivet in a riveted specimen. The responses are obtained in the cases of both healthy and faulty riveted specimens. The presence of a faulty rivet is indicated by the inflation in amplitude of the second harmonic. A new algorithm is therefore proposed by the authors to localize the faulty rivet, using the spectral content information. The results obtained through both the studies manifest the ability of the proposed methods for locating different types of defects and faulty rivets using an array of PW transducers.

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Wavelet-based built-in damage detection and identification for composites

Cited by 11 publications

References 5 publications

Intelligent defect location of a U-shaped boom using helical guided waves

Intelligent defect location of a U-shaped boom using helical guided waves

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

Localization of Damages in Plain And Riveted Aluminium Specimens using Lamb Waves

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