摘要
利用结构长期监测信号的多尺度特性,提出较为精确分离温度效应的自适应带宽滤波方法。按照多尺度分析思想,将温度和温度效应在日温差、年温差和聚然降温等不同时间尺度上展开,利用仅日温差效应与结构上的其他效应在时间尺度上具有不相耦合的特点,通过理论论证获得温度和温度效应具有线性相关特性,结合粒子群优化算法和滤波算法自适应改变日温差效应时间尺度的频率带宽,从而通过回归统计日温差和日温差效应达到精确分离监测信号中的温度效应之目的。分析结果表明:该方法较小波分析方法更能准确分离结构监测信号中的温度效应,为从长期监测信号中进行损伤识别提供了基础数据。
A new Adaptive Bandwidth Filter Method（ABFM） is proposed to separate the temperature effects from structural health monitoring signals.Using multi-scale analysis method,temperature are divided into daytime temperature variations,abrupt temperature drops and seasonal temperature fluctuations.Each category is associaed with distinct time scale.Daytime temperature effect does not couple with the other effects,and temperature is linearly related with structural effect proved by principle of virtual work.The ABFM adopts particle swarm optimization algorithm to adjust the frequency bandwidth of the daytime temperature effect.Therefore,the relationship between the temperature and the structure effect can be statistically determined more accurately.Simulation and experimental results show that the ABFM is advantageous over wavelet analysis in considering separating temperature effects.
出处
《工程力学》
EI
CSCD
北大核心
2010年第3期55-61,100,共8页
Engineering Mechanics
基金
国家863计划项目(2006AA04Z433)
关键词
多尺度分析
温度效应
自适应带宽
粒子群算法
统计回归
multi-scale analysis
temperature effect
adaptive bandwidth filter
particle swarm optimization
statistical regression
