摘要
[目的]舰船在执行作战任务时会受到导弹或炮弹的攻击,而穿甲损伤识别有其特殊性,对其进行专门研究具有重要意义。[方法]以舰船普遍存在的加筋板结构作为研究对象,首先,采用加筋板前3阶模态的固有频率作为特征参数的概率神经网络(PNN)对加筋板进行圆孔形穿甲损伤识别研究;然后,通过结合模态特征和加速度方差的结构损伤识别方法,解决固有频率的对称性识别问题。[结果]结果表明,所用方法对圆孔形穿甲损伤的定位和损伤程度识别效果良好。[结论]概率神经网络收敛速度快,易于在硬件上实现高度吻合的加筋板结构圆孔形穿甲损伤识别问题。
[Objectives]Warships will be attacked by missiles or shells when carrying out combat tasks,and armor piercing damage identification has certain characteristics,so it is of great significance to study this specific area.[Methods]Taking the stiffened plate structure which commonly exists in ships as the research object,a probability neural network(PNN)with the natural frequencies of the first three modes of stiffened plate as its characteristic parameters is used to study the damage identification of a stiffened plate with round holeshaped armor piercing;the symmetry identification of the natural frequencies is then solved by combining the structural damage identification method with the modal characteristics and acceleration variance.[Results]The results show that this method has a good effect on the damage location and damage degree identification of round hole-shaped armor piercing damage.[Conclusions]The PNN has such advantages as fast convergence,ease of implementation in hardware and high consistency with the problem of identifying round hole-shaped armor piercing damage in stiffened plate structures.
作者
吴昊
刘维勤
胡雨晨
WU Hao;LIU Weiqin;HU Yuchen(Key Laboratory of High Performance Ship Technology of Ministry of Education,Wuhan University of Technology,Wuhan 430063,China)
出处
《中国舰船研究》
CSCD
北大核心
2020年第2期88-94,共7页
Chinese Journal of Ship Research
基金
国家重点研发计划资助项目(2018YFB1601500)。
关键词
损伤识别
概率神经网络
固有频率
加筋板
圆孔形穿甲损伤
加速度方差
damage identification
probabilistic neural network(PNN)
natural frequency
stiffened panel
round hole shaped armor piercing damage
acceleration variance
