摘要
针对碳纤维复合材料层合板剪切过程中所产生的纤维断裂及基体开裂声发射信号的数据样本数量多、分布随机、变化形式较为离散等问题,提出一种可用于识别纤维断裂及基体开裂两种损伤类型的方法。首先,利用经验模态分解(EMD)对纤维断裂及基体开裂的声发射信号进行时频变换;然后,对分解后信号进行快速傅里叶变换(FFT)以获得特征频率集,再利用主成分分析法(PCA)对特征频率集进行降维处理;最后,利用支持向量机(SVM)实现纤维断裂及基体开裂信号进行损伤模式识别。结果表明,此方法可较为准确地识别纤维断裂及基体开裂两种信号。针对碳纤维复合材料层合板剪切过程所产生的声发射信号,模型的总识别率达85.8%。
Aiming at the problems of large number of data samples,random distribution and discrete variation of acoustic emission signals of fiber fracture and matrix cracking in carbon fiber composite laminates during shearing process,a method for two types of injury of identifying fiber breakage and matrix cracking is proposed.Firstly,adopting empirical mode decomposition(EMD)for time-frequency transform of the acoustic emission signals of fiber fracture and matrix cracking.Then,fast Fourier transform(FFT)is applied to the decomposed signal to obtain the characteristic frequency set,and principal component analysis(PCA)is used to reduce the dimension of the characteristic frequency set.Finally,the damage pattern recognition of fiber fracture and matrix cracking signal is realized by support vector machine(SVM).The results indicate that this method can identify fiber fracture and matrix cracking signals accurately.The total recognition rate of the model is 85.8%specific to the acoustic emission signal generated during shearing of carbon fiber composite laminates.
作者
李伟
姜智通
张璐莹
蒋鹏
LI Wei;JIANG Zhitong;ZHANG Luying;JIANG Peng(School of Mechanical Science and Engineering,Northeast Petroleum University,Daqing 163318,China)
出处
《中国测试》
CAS
北大核心
2020年第6期121-128,共8页
China Measurement & Test
基金
国家重点研发计划(2017YFC0805600)。
关键词
碳纤维复合材料
声发射
纤维断裂
经验模态分解
主成分分析
支持向量机
carbon fiber reinforced polymer
acoustic emission
fiber breakage
empirical mode decomposition
principal component analysis
support vector machine
