基于LTSA和RVM的复合材料损伤预测分析

Damage Prediction Analysis and Verification of the Composite Material Based on LTSA and RVM

提出一种基于局部切空间排序(local tangent space alignment,简称LTSA)和相关向量机(relevance uector machine,简称RVM)相结合的复合材料结构损伤演化与预测模型。针对复合材料结构损伤特性,采用疲劳振动试验进行结构损伤预测研究。首先,采用总体平均经验模态分解(ensemble empirical mode decomposition,简称EEMD)方法对多传感器采集的复合材料结构健康信息进行自适应分解,得到不同传感器下的多个本征模态分量(intrinsic mode function,简称IMF),并对IMF进行希尔伯特(Hilbert)变换,得到相应的Hilbert边际谱能量作为各传感器的特征信息;然后,采用LTSA进行多特征降维融合得到特征能量,对降维融合后得到特征能量采用距离形态相似度方法定义结构健康指数;最后,将结构健康指数作为建模数据,创建RVM预测模型,并通过预测结构健康指数完成复合材料结构损伤预测研究。验证结果表明,该模型可有效地对复合材料结构损伤进行预测。

This paper proposes an evolution and prediction model of composite structural damage based on the approach of combining local tangent space alignment (LTSA) with relevance vector machine (RVM). In light of the characteristics of composite structural damage, we used a vibration fatigue test to predict structural damage. First, we acquired the health information of the composite structure via multi-sensor decomposed into intrinsic mode function (IMF) by ensemble empirical mode decomposition (EEMD). We then applied Hilbert-Huang transform (HHT) to IMF and obtained corresponding Hilbert marginal energy as the characteristics of each sensor. Second, we made feature fusion in order to obtain the characteristic energy using LTSA, and defined the resulting characteristic energy as the structure of the health index using the DMS method. Finally, regarding the structure of the health index as modeling data, we established a relevance vector machine (RVM) model, and completed the composite structural damage prediction research by predicting the structure of the health index. Results showed that this model can be used effectively to predict structural damage. © 2017, Editorial Department of JVMD. All right reserved.