摘要
直升机旋翼桨叶在飞行过程中极易发生疲劳损伤,为了解决桨叶损伤源定位问题,构建了桨叶损伤监测及定位系统。通过核主成分分析(KPCA)对损伤源的声发射信号进行特征提取,结合支持向量机(SVM)及其回归功能对旋翼桨叶模型损伤源进行定位。使用特征提取后的参数区域损伤定位精度达到100%,回归分析平均误差率4.06%,均优于使用原始数据进行定位,因此该方法能够有效实现直升机旋翼桨叶损伤源定位,并且减少了输入数据的维数,降低了计算量。
Helicopter rotor blades are prone to fatigue damage in flight.To solve the damage location problem,a damage monitoring and locating system was constructed.With the acoustic emission signals of the damage sources extracted by the kernel principal component analysis(KPCA),combining the support vector machine(SVM)and its regression function,the damage sources of the rotor blades were located.The regional location accuracy after feature extraction is 100%and the average regression error is 7%,which are better than the original data location.Therefore,this method can effectively locate the damage source of the rotor blade,reduce the dimension of input data and the amount of calculation.
作者
叶杨
徐志伟
陈仁文
刘宋祥
Ye Yang;Xu Zhiwei;Chen Renwen;Liu Songxiang(State Key Laboratory of Mechanics and Control of Mechanical Structures,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2020年第4期118-123,共6页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(51675265)
江苏省高校优势学科建设工程(PAPD)
机械结构力学及控制国家重点实验室自主研究课题(0515K01)资助项目。
关键词
声发射
损伤定位
核主成分分析
支持向量机
特征提取
acoustic emission
damage location
kernel principal component analysis(KPCA)
support vector machine(SVM)
feature extraction
