基于SPSO-WK-TWSVM的复合材料层合板损伤辨识方法

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摘要针对复合材料层合板的基体裂纹损伤与脱层损伤的不易区分辨识的问题，采用Lamb波对层合板进行损伤检测，对接收到的传感信号进行特征提取与筛选，创新性地引入加权核双子支持向量基（weighted kernels -twin support vector machine，WK-TWSVM）的机器学习方法对基体裂纹与脱层损伤进行自动分类识别。为了进一步提高损伤辨识精度，采用简化粒子群优化（simple particle swarm optimization,SPSO）算法对WK-TWSVM的核函数权值及模型参数进行了寻优处理，并与其他粒子群优化算法就行了分析比较。试验分析结果表明，基于Lamb波的SPSO-WK-TWSVM复合材料层合板损伤辨识方法能够对复合材料层合板基体裂纹与脱层损伤进行准确的自动识别，识别精度明显高于其他TWSVM优化算法及传统的机器学习方法。

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	刘小峰
	王邦昕
	艾帆
	韦代平

关键词 ：复合材料层合板, Lamb波, 损伤分类辨识, 简化粒子群优化, 双子支持向量基

Abstract：Aiming at the problem of it being difficult to distinguish matrix crack damage and delamination damage of composite laminates, Lamb wave was used to detect damages of composite laminates, feature extraction and screening of the received sensing signals were conducted, and the machine learning method based on weighted kernels-twin support vector machine (WK-TWSVM) was introduced innovatively to automatically classify and identify matrix crack and delamination damage. In order to further improve the accuracy of damage recognition, the simple particle swarm optimization (SPSO) algorithm was used to optimize kernel functions’ weights and model parameters, and SPSO algorithm was compared with other PSO algorithms. Test results showed that the damage identification method based on SPSO-WK-TWSVM and Lamb wave can automatically identify matrix crack and delamination damage of composite laminates, and its recognition accuracy is obviously higher than those of other TWSVM optimization algorithms and traditional machine learning methods.

Key words： composite laminate Lamb wave damage classification identification simple particle swarm optimization (SPSO) algorithm weighted kernels-twin support vector machine (WK-TWSVM)

收稿日期: 2020-02-17 出版日期: 2021-08-15

引用本文:

刘小峰，王邦昕，艾帆，韦代平. 基于SPSO-WK-TWSVM的复合材料层合板损伤辨识方法[J]. 振动与冲击, 2021, 40(15): 290-295.
LIU Xiaofeng, WANG Bangxin, AI Fan, WEI Daiping. Damage identification of composite laminates based on SPSO-WK-TWSVM. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(15): 290-295.

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http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2021/V40/I15/290

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