基于模态误差函数灵敏度分析的损伤识别方法
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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。
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.
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.
引文
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[16]Caicedo M,Dyke S J.Two-stage bayesian structuralhealth monitoring approach for phase ASCE Bench-mark Studies[A].Proceedings of IMAC-XXI[C].Florida,February,2003.
[2]马宏伟,杨桂通.结构损伤探测的基本方法和研究进展[J].力学进展,1999,29(4):513—527.
[3]李宏男,李东升.土木工程结构安全性评估、健康监测及诊断述评[J].地震工程与工程振动,2002,22(3):82—90.
[4]韩大建,王文东.基于振动的结构损伤识别方法的近期研究进展[J].华南理工大学学报(自然科学版),2003,30(1):91—96.
[5]Berman A,Nagy E J.Improvement of large analyticalmodel using test data[J].AIAA Journal,1983,21(8):1 168—1 173.
[6]Guyan R J.Reduction of stiffness and mass metrices[J].AIAA Journal,1965,3(2):380.
[7]Kim H M,Bartkowicz T J.Damage detection andhealth monitoring of large space structure[J].Journalof Sound and Vibration,1993,27(6):12—17.
[8]Hjelmstad K D.On the uniqueness of modal parame-ter estimation[J].J.Sound Vibration,1996,192(2):581—598.
[9]秦权,张卫国.悬索桥的损伤识别[J].清华大学学报,1998,38(12):44—47.
[10]Thanyawat Pothisiri.Structural Damage Detectionand Assessment from Spatially Sparse and Noise-pol-luted Modal Response[D].Dept.of Civil Eng.,Illi-nois Univ.,US.,2001.
[11]Fritzen C P,Bohle K.Parameter selection strategiesin model-based damage detection[A].StructuralHealth Monitoring 2000[C].Stanford University,Pa-lo Alto,California,1999;901—911.
[12]徐宜桂,史铁林,杨叔子.基于神经网络的结构模型修改和破损诊断研究[J].振动工程学报,1997,10(1):8—12.
[13]瞿伟廉,黄东梅.高耸塔架结构节点损伤基于神经网络的两步诊断法[J].地震工程与工程振动,2003,23(2):143—149.
[14]Sutter T R,Camarda C J,Walsh J L,et al.Compar-ison of several methods for calculating vibration modeshape derivatives[J].AIAA Journal,1998,26(12):1 506—1 511.
[15]孙国,顾元宪.连续梁结构损伤识别的改进柔度阵方法[J].工程力学,2003,20(4):50—54.
[16]Caicedo M,Dyke S J.Two-stage bayesian structuralhealth monitoring approach for phase ASCE Bench-mark Studies[A].Proceedings of IMAC-XXI[C].Florida,February,2003.
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