基于GRNN-ELM的飞机复合材料结构损伤识别

Damage Identification of Aircraft Composite Structure Based on GRNN-ELM

飞机结构的损伤严重影响着飞机的飞行安全,为了解决飞机复合材料结构损伤难以有效识别问题,本文提出一种基于广义回归神经网络(General regression neural network,GRNN)与极限学习机(Extreme learning machine,ELM)组合的飞机复合材料结构损伤识别新方法。首先对飞机复合材料层合板进行冲击,而后对其进行疲劳拉伸试验,通过优化布局在复合材料层合板上的光纤光栅传感器募集应变信息,并对其进行预处理。采用变分模态分解(Variational mode decomposition,VMD)对募集的应变信息进行自适应分解,得到多个基本模式分量(Intrinsic mode function,IMF)。计算各阶IMF分量的奇异熵,通过核独立主元分析(Kernel independent component analysis,KICA)方法对奇异熵进行特征融合,构建融合特征向量。采用融合特征向量建立基于GRNN-ELM的复合材料结构损伤识别模型,通过试验对损伤识别模型的有效性进行了验证,并分别与所构建的ELM和GRNN损伤识别模型的识别结果进行比较。结果表明,该方法能有效对飞机复合材料结构损伤进行识别,具有很好的工程应用价值。

Aircraft structure damage seriously affects the aircraft flight safety.In order to effectively identify aircraft composite structure damage,a new method combining general regression neural network（GRNN）and extreme learning machine（ELM）of composite structure damage diagnosis is proposed in this paper.Firstly,the data of fiber optic sensor on composite material laminated plates are gathered and pre-processed after striking and stretching on composite laminated plates.Secondly,strain information is decomposed by variational mode decomposition（VMD）,and intrinsic mode functions（IMFs）are obtained.Meanwhile,the singular entropy feature of each IMF is derived.Then,a featurevector is built by kernel independent component analysis（KICA）.Finally,the fusion feature vector is used to build GRNN-ELM identification model.Experimental data verify the effectiveness of the GRNNELM method,and the result shows that the GRNN-ELM model can realize aircraft composite structure damage identification more effectively compared with ELM and GRNN models,respectively,thus it has agood engineering application value.