Guided Wave Based Composite Structural Fatigue Damage Monitoring Utilizing the WOA-BP Neural Network  

作　　者：Borui Wang Dongyue Gao Haiyang Gu Mengke Ding Zhanjun Wu 

机构地区：[1]College of Fiber Engineering and Equipment Technology,Jiangnan University,Wuxi,214122,China

出　　处：《Computers, Materials & Continua》2025年第4期455-473,共19页计算机、材料和连续体(英文)

基　　金：funded by the Key Program of the National Natural Science Foundation of China(U2341235);Youth Fund for Basic Research Program of Jiangnan University(JUSRP123003);Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1237);the National Key R&D Program of China(2018YFA0702800);Key Technologies R&D Program of CNBM(2023SJYL01).

摘　　要：Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.

关 键 词：Structural health monitoring ultrasonic guided wave composite structural fatigue damage monitoring WOA-BP neural network relief-F algorithm 

分 类 号：TB332[一般工业技术—材料科学与工程]