摘要
提出了针对时变系统响应的短时频率线性时变假设,通过将时变响应拟合成多分量线调频信号,根据线调频信号互相关理论推导了随机白噪声激励下时变系统的物理参数识别方法。该识别方法只需基于结构的加速度响应,便能识别结构的时变质量和刚度。由于引入了调频斜率刻画响应信号的短时频率线变特征,该方法相比传统识别方法能更好地追踪快变甚至突变参数,对实际工程中的时变问题具有重要的应用价值。仿真算例中构造了1个3自由度时变结构模型,针对线性时变、周期时变和突变等情况进行了物理参数的识别,误差分析显示识别误差均在5%以内,仿真结果验证了方法的正确性和适用性。
The assumption that frequencies of responses of linear time-varying systems vary linearly in a short-time was proposed.By fitting the time-varying responses to multicomponent line frequency modulation signals,aphysical parameter identification method of time-varying systems under random white noise excitation was obtained according to the cross-correlation theory of line frequency modulation signals.The mass and stiffness coefficients of time-varying structures were identified only using the acceleration response.Due to the introduction of frequency modulation factor to describe the variation of frequencies of response signals in a short-time,this method could track fast changes or even abrupt changes of parameters better than traditional identification method,so it had important application value for time-varying structures in practical engineering.In the simulation example,a 3-degress of freedom time-varying structure model was constructed to identify linear,periodic and abrupt changes of physical parameters.The error analysis showed that the identification errors were all within 5%.The results verified the correctness and applicability of the method.
作者
张杰
史治宇
李利荣
ZHANG Jie;SHI Zhiyu;LI Lirong(State Key Laboratory of Mechanics and Control of Mechanical Structures,College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;Shanghai Aircraft Customer Service Company Limited,Commercial Aircraft Corporation of China,Limited,Shanghai 200241,China)
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2020年第1期9-17,共9页
Journal of Aerospace Power
基金
国家自然科学基金(11172131,11232007)
江苏高校优势学科建设工程.
关键词
线调频小波变换
加速度响应
时变系统参数识别
随机激励
相关函数
chirplet transform
acceleration response
parameter identification of time-varying systems
random excitation
correlation function