【摘要】 利用非完整的模态响应信息进行损伤识别,通常归结为一个基于模态参数匹配的优化问题,较多的待识别参数将导致优化反问题计算规模过大以及识别结果非唯一性等问题。本文提出了基于误差函数灵敏度分析的新的参数更新策略。该方法将少数优势单元参数的提取与单元参数参考值的改进结合起来,实现逐次扩增的参数分组过程,每一步所采用的优化反问题中,只求解较小规模的优化问题。基于所建立的参数识别方法,数值仿真结果显示所采用的模型更新方法可以有效处理稀疏的有噪音测试数据,具有较稳健的识别性能。更多还原

【Abstract】 For damage identification from sparse modal response,many damage identification methods attempt to minimize a no-linear error function between the analytical and measured incomplete modal parameters.For the identification methods,the main problem is the multiplicity of parameter estimation solutions arising from the use of spatially sparse and noised-polluted data.A new parameter reduction scheme is proposed based on sensitivity analysis of modal parameter no-linear error function.The proposed scheme detects the dominance element parameters and updates the reference parameter values sequentially and then the parameter group is expanded and updated.In the certain updating step,only a small scale optimization solution procedure is involved.Based on the proposed parameter updating scheme,the damage identifications on a freely supported beam with multi damages are studied.The simulation results of the model update strategy show robust property to processing spatially sparse and noised-polluted data.更多还原