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

Abstract

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.

Keywords

Lamb waves evolutionary strategy clustering algorithm defect location intelligent detection

Get full access to this article

View all access options for this article.

References

Liu

Chen

HL.

Application and challenges of signal processing techniques for Lamb waves structural integrity evaluation: part A —Lamb waves signals emitting and optimization techniques. In: Wahab

Zhou

Maia

NMM

(eds) Structural Health Monitoring from Sensing to Processing. London: IntechOpen, 2018, pp. 61–86.

Liu

Chen

HL.

Application and challenges of signal processing techniques for Lamb waves structural integrity evaluation: part B —defects imaging and recognition techniques. In: Wahab

Zhou

Maia

NMM

(eds) Structural Health Monitoring from Sensing to Processing. London: IntechOpen, 2018, pp. 87–115.

Stepinski

Ambrozinski

Uhl

. Designing 2D arrays for SHM of planar structures: a review. In: Conference on nondestructive characterization for composite materials, aerospace engineering, civil infrastructure, and homeland security, San Diego, CA, 11–14 March 2013, pp. 12. Bellingham, WA: SPIE.

Michaels

Hall

Michaels

TE.

Adaptive imaging of damage from changes in guided wave signals recorded from spatially distributed arrays. Proc SPIE 2009; 7295: 729515.

Camacho

Parrilla

Fritsch

Phase coherence imaging. IEEE Trans Ultrason Ferroelectr Freq Control 2009; 56(5): 958–974.

Liu

Chen

Sun

, et al. Full non-contact laser-based Lamb waves phased array inspection of aluminum plate. J Vis 2018; 21(5): 751–761.

Liu

Sun

Song

, et al. Damage localization in aluminum plate with compact rectangular phased piezoelectric transducer array. Mech Syst Signal Proc 2016; 70–71: 625–636.

Prado

Higuti

Kitano

, et al. Lamb mode diversity imaging for non-destructive testing of plate-like structures. NDT&E Int 2013; 59: 86–95.

Higuti

Martinez-Graullera

Martin

, et al. Damage characterization using guided-wave linear arrays and image compounding techniques. IEEE Trans Ultrason Ferroelectr Freq Control 2010; 57(9): 1985–1995.

10.

Prado

Higuti

Kitano

, et al. Sparse arrays and image compounding techniques for non-destructive testing using guided acoustic waves. J Control Autom Elect Sys 2013; 24(3): 263–271.

11.

Liu

Wei

, et al. Image fusion based on single-frequency guided wave mode signals for structural health monitoring in composite plates. Mater Eval 2013; 71(12): 1434–1443.

12.

Muller

Robertson-Welsh

Gaydecki

, et al. Structural health monitoring using Lamb wave reflections and total focusing method for image reconstruction. Appl Compos Mater 2017; 24(2): 553–573.

13.

Croxford

Wilcox

Drinkwater

, et al. Strategies for guided-wave structural health monitoring. Proc R Soc A: Math Phys Eng Sci 2007; 463(2087): 2961–2981.

14.

Michaels

TE.

Guided wave signal processing and image fusion for in situ damage localization in plates. Wave Motion 2007; 44(6): 482–492.

15.

Shan

Qiu

Zhang

, et al. Multi-damage localization on large complex structures through an extended delay-and-sum based method. Struct Health Monit 2016; 15(1): 50–64.

16.

Flynn

Todd

Wilcox

, et al. Maximum-likelihood estimation of damage location in guided-wave structural health monitoring. Proc R Soc A: Math Phys Eng Sci 2011; 467(2133): 2575–2596.

17.

Gao

Yang

, et al. Guide waves-based multi-damage identification using a local probability-based diagnostic imaging method. Smart Mater Struct 2016; 25: 45009.

18.

Dokeroglu

Sevinc

Kucukyilmaz

, et al. A survey on new generation metaheuristic algorithms. Comput Ind Eng 2019; 137: 106040.

19.

Satyanarayan

Kumaran

Krishnamurthy

, et al. Inverse method for detection and sizing of cracks in thin sections using a hybrid genetic algorithm based signal parametrisation. Theor Appl Fract Mech 2008; 49(2): 185–198.

20.

Demirli

Saniie

Asymmetric Gaussian chirplet model and parameter estimation for generalized echo representation. J Frankl Inst 2014; 351(2): 907–921.

21.

Bustillo

Fortineau

Gautier

, et al. Ultrasonic characterization of porous silicon using a genetic algorithm to solve the inverse problem. NDT&E Int 2014; 62: 93–98.

22.

Marzani

De Marchi

Characterization of the elastic moduli in composite plates via dispersive guided waves data and genetic algorithms. J Intel Mat Syst Str 2013; 24(17): 2135–2147.

23.

Gomes

de Almeida

Junqueira

, et al. Optimized damage identification in CFRP plates by reduced mode shapes and GA-ANN methods. Eng Struct 2019; 181: 111–123.

24.

Bouzenad

AEEI

Mountassir

Yaacoubi

, et al. A semi-supervised based K-means algorithm for optimal guided waves structural health monitoring: a case study. Inventions 2019; 4(1): 1–18.

25.

Yacef

Bouden

Grimes

Accurate ultrasonic measurement technique for crack sizing using envelope detection and differential evolution. NDT&E Int 2019; 102: 161–168.

26.

Chen

Zhou

Chen

, et al. Detection of double defects for plate-like structures based on a fuzzy c-means clustering algorithm. Struct Health Monit 2019; 18(3): 757–766.

27.

Yan

Zhou

Yuan

. Wavelet-based built-in damage detection and identification for composites. In: Smart structures and materials 2005 conference, San Diego, CA, 7–10 March 2005, pp. 324–334. Bellingham, WA: SPIE.

28.

Andhale

Masurkar

Yelve

NP.

Localization of damages in plain and riveted aluminium specimens using Lamb waves. Int J Acoust Vib 2019; 24(1): 150–165.

29.

Fogel

DB.

An introduction to simulated evolutionary optimization. IEEE Trans Neur Net 1994; 5(1): 3–14.

30.

Sinaga

Yang

MS.

Unsupervised K-means clustering algorithm. IEEE Access 2020; 8: 80716–80727.

31.

Lloyd

SP.

Least squares quantization in PCM. IEEE Trans Inf Theory 1982; 28(2): 129–137.