基于GDF的结构载荷及参数联合识别的实时方法
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摘要
结构载荷及参数识别是结构动力学领域的重要研究内容,目前已有一些研究致力于两者的联合识别。比如EGDF法,虽然可以连续识别未知载荷和参数,但需要载荷自由度上的加速度响应;另外,该方法与其他基于最小二乘的连续识别方法一样,存在未知载荷和位移识别的低频漂移现象。提出了上述问题的改进方法,将识别问题转化为模态空间中的模态位移以及模态载荷的识别,再利用模态缩减法,得到实时的EGDF法;另外,采用位移和加速度作为测量响应,可解决识别结果的低频漂移问题。
Structural load and parametric identification are important contents in structural dynamics field. At present, there are some studies focusing on joint identification of them. The extended GDF(EGDF) method presented here by the authors has the ability to continuously identify unknown loads and parameters. However, it is necessary to know acceleration responses at locations unknown loads exerted on. Besides, like other identification methods based on the least square, the EGDF method has low frequency drift phenomena of identified unknown loads and displacements. Here, a modified method was proposed to convert this recognition problem into the recognition of modal displacements and modal loads in modal space, and then the modal truncation technique was used to obtain the real-time EGDF method. Furthermore, taking displacement and acceleration as measured responses, the data fusion technique was used to solve low frequency drift problems in identification results.
Structural load and parametric identification are important contents in structural dynamics field. At present, there are some studies focusing on joint identification of them. The extended GDF(EGDF) method presented here by the authors has the ability to continuously identify unknown loads and parameters. However, it is necessary to know acceleration responses at locations unknown loads exerted on. Besides, like other identification methods based on the least square, the EGDF method has low frequency drift phenomena of identified unknown loads and displacements. Here, a modified method was proposed to convert this recognition problem into the recognition of modal displacements and modal loads in modal space, and then the modal truncation technique was used to obtain the real-time EGDF method. Furthermore, taking displacement and acceleration as measured responses, the data fusion technique was used to solve low frequency drift problems in identification results.
引文
[1] 马超,华宏星.基于改进正则化方法的状态空间载荷识别技术[J].振动与冲击,2015,34(11):146-149.MA Chao,HUA Hongxing.State space load identification technique based on an improved regularized method[J].Journal of Vibration and Shock,2015,34(11):146-149.
[2] 马超,华宏星.正则化技术在状态空间载荷识别中的应用[J].振动、测试与诊断,2014,34(6):1154-1158.MA Chao,HUA Hongxing.State space force identification based on regularization technique[J].Journal of Vibration,Measurement & Diagnosis,2014,34(6):1154-1158.
[3] LIU X,ESCAMILLA-AMBROSIO P J,LIEVEN N.Extended Kalman filtering for the detection of damage in linear mechanical structures[J].Journal of Sound and Vibration,2009,325(4):1023-1046.
[4] LIU J,SUN X,HAN X,et al.A novel computational inverse technique for load identification using the shape function method of moving least square fitting[J].Computers & Structures,2014,144:127-137.
[5] 韩旭,刘杰,李伟杰,等.时域内多源动态载荷的一种计算反求技术[J].力学学报,2009,41(4):595-602.HAN Xu,LIU Jie,LI Weijie,et al.A computational iinverse technique for reconstruction multisource loads in time domain[J].Chinese Journal of Theoretical and Applied Mechanics,2009,41(4):595-602.
[6] 杨智春,贾有.动载荷的识别方法[J].力学进展,2015,45:29-54.YANG Zhichun,JIA You.The identification of dynamic loads[J].Advances in Mechanics,2015,45:29-54.
[7] YANG J N,PAN S,HUANG H.An adaptive extended Kalman filter for structural damage identifications II:unknown inputs[J].Structural Control and Health Monitoring,2007,14(3):497-521.
[8] LEI Y,JIANG Y,XU Z.Structural damage detection with limited input and output measurement signals[J].Mechanical Systems and Signal Processing,2012,28:229-243.
[9] LEI Y,WU Y,LI T.Identification of non-linear structural parameters under limited input and output measurements[J].International Journal of Non-Linear Mechanics,2012,47(10):1141-1146.
[10] NAETS F,CROES J,DESMET W.An online coupled state/input/parameter estimation approach for structural dynamics[J].Computer Methods in Applied Mechanics and Engineering,2015,283:1167-1188.
[11] WAN Z,WANG T,HUANG Q,et al.Simultaneous excitation and parameter identification for non-linear structural systems[J].Journal of Vibroengineering,2017,19(6):3968-3980.
[12] AZAM S E,CHATZI E,PAPADIMITRIOU C.A dual Kalman filter approach for state estimation via output-only acceleration measurements[J].Mechanical Systems and Signal Processing,2015,60:866-886.
[2] 马超,华宏星.正则化技术在状态空间载荷识别中的应用[J].振动、测试与诊断,2014,34(6):1154-1158.MA Chao,HUA Hongxing.State space force identification based on regularization technique[J].Journal of Vibration,Measurement & Diagnosis,2014,34(6):1154-1158.
[3] LIU X,ESCAMILLA-AMBROSIO P J,LIEVEN N.Extended Kalman filtering for the detection of damage in linear mechanical structures[J].Journal of Sound and Vibration,2009,325(4):1023-1046.
[4] LIU J,SUN X,HAN X,et al.A novel computational inverse technique for load identification using the shape function method of moving least square fitting[J].Computers & Structures,2014,144:127-137.
[5] 韩旭,刘杰,李伟杰,等.时域内多源动态载荷的一种计算反求技术[J].力学学报,2009,41(4):595-602.HAN Xu,LIU Jie,LI Weijie,et al.A computational iinverse technique for reconstruction multisource loads in time domain[J].Chinese Journal of Theoretical and Applied Mechanics,2009,41(4):595-602.
[6] 杨智春,贾有.动载荷的识别方法[J].力学进展,2015,45:29-54.YANG Zhichun,JIA You.The identification of dynamic loads[J].Advances in Mechanics,2015,45:29-54.
[7] YANG J N,PAN S,HUANG H.An adaptive extended Kalman filter for structural damage identifications II:unknown inputs[J].Structural Control and Health Monitoring,2007,14(3):497-521.
[8] LEI Y,JIANG Y,XU Z.Structural damage detection with limited input and output measurement signals[J].Mechanical Systems and Signal Processing,2012,28:229-243.
[9] LEI Y,WU Y,LI T.Identification of non-linear structural parameters under limited input and output measurements[J].International Journal of Non-Linear Mechanics,2012,47(10):1141-1146.
[10] NAETS F,CROES J,DESMET W.An online coupled state/input/parameter estimation approach for structural dynamics[J].Computer Methods in Applied Mechanics and Engineering,2015,283:1167-1188.
[11] WAN Z,WANG T,HUANG Q,et al.Simultaneous excitation and parameter identification for non-linear structural systems[J].Journal of Vibroengineering,2017,19(6):3968-3980.
[12] AZAM S E,CHATZI E,PAPADIMITRIOU C.A dual Kalman filter approach for state estimation via output-only acceleration measurements[J].Mechanical Systems and Signal Processing,2015,60:866-886.