工程科学与技术2026，Vol.58Issue(3)：48-58,11.DOI:10.12454/j.jsuese.202500063
基于Bayes数据融合和改进鲸鱼算法的可展开天线结构损伤识别研究
Research on Damage Identification of Deployable Antenna Structure Based on Bayes Data Fusion and Improved Whale Optimization Algorithm
摘要
Abstract
Objective With increasingly stringent requirements for satellite precision in fields such as deep space exploration,remote sensing and navigation,and military reconnaissance,large-aperture modular deployable antennas are becoming a key development trend in astronautical deployable an-tenna systems.However,due to factors such as the disintegration or explosion of defunct spacecraft,the number of small space debris objects con-tinues to increase year by year,posing a significant threat to their on-orbit service.There is currently limited research on damage identification for large and complex structures,such as deployable antennas.In such cases,accurately identifying damage presents significant challenges,often leading to the misclassification of undamaged components and resulting in reduced identification accuracy.In this paper,an improved two-stage structural damage identification method is proposed by combining Bayesian data fusion and an improved whale optimization algorithm.Numeri-cal simulations of damage identification are carried out on a large-aperture space deployable antenna support structure with 415 rods to verify the identification accuracy of the two-stage method under a certain level of noise interference. Methods First,six damage conditions are designed based on three types of damage:single damage,double damage,and multiple damage.The Bayesian data fusion method is adopted to integrate the cross-model modal strain energy index(CMSEI)and the cross-model modal strain energy change rate index(CMSECR).A damage threshold is defined,and rods with values exceeding this threshold are identified as suspected damaged rods,thereby enabling localization of structural damage.The localization accuracy of Bayesian data fusion is then compared with that of using only CMSEI under noise levels of 0,0.1%,and 0.2%.Second,to overcome the limitations of the whale optimization algorithm(WOA),such as its tendency to fall into local optima and its insufficient global search capability,a hybrid multi-strategy whale optimization algorithm(HMWOA)is proposed.This approach incorporates multiple improvement strategies,including Sobol sequence-based population initialization and parallel search with dual populations and communication mechanisms.The performance of the proposed algorithm is compared with that of WOA and five other optimization algorithms using six benchmark test functions.Finally,after Bayesian data fusion is used to identify the suspected dam-aged rods of the deployable antenna support structure in the first stage,the HMWOA algorithm is applied in the second stage to quantify the dam-age.This process further eliminates non-damaged rods and determines the damage severity of the actual damaged rods. Results and Discussion From the damage localization results of single-damage scenario 1,it can be seen that both methods,CMSEI and Bayes-ian data fusion,can clearly identify the actual damaged rods at noise levels of 0,0.1%,and 0.2%.However,Bayesian data fusion achieves more accurate damage localization and reduces the possibility of misclassification,especially at a noise level of 0.2%.From the damage localization re-sults of multi-damage scenarios 4 to 6,it can be observed that the sensitivity of different rod types to structural damage varies.The central vertical rods are the least sensitive to damage,followed by the edge vertical rods,while the chord and diagonal web rods are more sensitive.Therefore,it is necessary to define different damage thresholds for different types of rods.Based on the results of the three working conditions,the damage thresholds are set to 0.001 for central vertical rods,0.03 for edge vertical rods,and 0.2 for chord and diagonal web rods.The effect of noise on multi-damage scenarios is more significant than that on single-damage scenarios,increasing the number of non-damaged rods misclassified by both methods for damage localization.However,the use of Bayesian data fusion effectively reduces the effect of noise.For damage scenario 4,the damage identification results using CMSEI show that,under 0.2%noise,31 rods are identified as suspected damaged rods,compared to 8 rods under noise-free conditions.In contrast,using Bayesian data fusion under 0.2%noise results in 11 suspected damaged rods,while only 3 rods are identified in the noise-free case.To verify the solution accuracy and convergence speed of HMWOA,we compare the results of WOA,an improved whale optimization algorithm(MSWOA),the sailfish optimization algorithm(SFO),the sparrow search algorithm(SSA),and the ze-bra optimization algorithm(ZOA).Six benchmark test functions from CEC2005 are selected to evaluate the performance of the different algo-rithms.To reduce the influence of randomness on the experimental results,we conduct 30 independent runs and calculate the mean and standard deviation of the solutions.The results indicate that the HMWOA algorithm demonstrates superior accuracy and stability in optimizing functions F1 to F6.In particular,for functions F3,F5,and F6,the advantage of the HMWOA algorithm is most pronounced.Compared with other algo-rithms,HMWOA also maintains the fastest optimization speed across all six benchmark test functions,significantly reducing computation time.In the damage quantification stage,due to the high accuracy of damage localization using Bayesian data fusion at noise levels of 0 and 0.1%,only the case with a noise level of 0.2%is examined.From the three scenarios of single-damage scenario 1,double-damage scenario 3,and multi-damage scenario 4,it can be seen that,after using Bayesian data fusion in the first stage to identify suspected damaged rods,the HMWOA algo-rithm in the second stage can further identify the actual damaged rods and accurately determine the damage severity.The damage quantification error is kept within 5%,and no non-damaged rods are misclassified. Conclusion The results show that using Bayesian data fusion to integrate CMSEI and CMSECR for damage localization yields more accurate identification results.The four improvement strategies effectively enhance the optimization accuracy and convergence speed of the WOA.For complex structures with a large number of rods,the two-stage damage identification method based on Bayesian data fusion and the improved WOA proposed in this paper can accurately identify structural damage.关键词
结构损伤识别/Bayes数据融合/改进鲸鱼算法/跨模型模态应变能/可展开天线结构Key words
structural damage identification/Bayes data fusion/improved whale optimization algorithm/cross-model modal strain energy/de-ployable antenna structure分类
航空航天引用本文复制引用
金路,刘安清,田大可,赵丙峰..基于Bayes数据融合和改进鲸鱼算法的可展开天线结构损伤识别研究[J].工程科学与技术,2026,58(3):48-58,11.基金项目
辽宁省教育厅领军人才团队项目(U222410153096) （U222410153096）
东北大学航空动力装备振动及控制教育部重点实验室研究基金项目(VCAME202207) （VCAME202207）
国家自然科学基金联合基金项目(U2341237) （U2341237）
