基于联邦扩展卡尔曼滤波的结构损伤识别方法
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摘要
当结构损伤与传感器故障同时存在时,两者的相互影响会导致损伤识别结果的劣化;为此,提出了一种基于联邦扩展卡尔曼滤波的结构损伤识别算法。利用分散化滤波计算量小、滤波精度高的优点,联邦扩展卡尔曼滤波方法能根据正常的传感器信号准确识别结构的损伤位置与程度,具有良好的鲁棒性;同时利用联邦滤波容错性好的特点,能实现对故障信号的自动检测和剔除,并将剩余的正常子系统进行组合,以继续提供准确的损伤识别结果。梁式结构的数值算例及实验分析验证了该算法的有效性及对故障传感器信号的检测隔离能力。
The coexistence of structure damages and sensor faults will deteriorate identified results evidently,so an algorithm for the identification of structure damages based on the Federated Extended Kalman Filter method( FEKF) was proposed by using free vibration signals. The presented method can identify the location and extent of damages accurately,and shows good robustness when the sensors work normally. Combined with the residual chi-square test,the FEKF also can eliminate the effects of fault sensors by the automatic detection and removal of the fault sensor signals. Numerical simulations and experiments show that the FEKF can ensure the accuracy and stability of the damage identification results and detect fault signals effectively.
The coexistence of structure damages and sensor faults will deteriorate identified results evidently,so an algorithm for the identification of structure damages based on the Federated Extended Kalman Filter method( FEKF) was proposed by using free vibration signals. The presented method can identify the location and extent of damages accurately,and shows good robustness when the sensors work normally. Combined with the residual chi-square test,the FEKF also can eliminate the effects of fault sensors by the automatic detection and removal of the fault sensor signals. Numerical simulations and experiments show that the FEKF can ensure the accuracy and stability of the damage identification results and detect fault signals effectively.
引文
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[11]戴霖.基于扩展卡尔曼滤波的结构损伤识别方法[D].南昌:南昌大学,2014.
[12]WEBER B,PAULTRE P,PROULX J.Consistent regularization of nonlinear model updating for damage identification[J].Mechanical Systems and Signal Processing,2009,23(6):1965-1985.
[2]CALABRESE A,SERINO G,STRANO S,et al.An extended Kalman Filter procedure for damage detection of base-isolated structures[P].2014 IEEE Workshop on Environmental Energy and Structural Monitoring Systems(EESMS),Naples,Italy.IEEE,USA,2014.
[3]YANG J N,HUANG H.Sequential non-linear least-square estimation for damage identification of structures with unknown inputs and unknown outputs[J].International Journal of Nonlinear Mechanics,2007,42(5):789-801.
[4]樊素英,李忠献.基于广义卡尔曼滤波的桥梁结构物理参数识别[J].计算力学学报,2007,24(4):472-476.FAN Suying,LI Zhongxian.Identification on physical parameters of bridge structures based on extended Kalman filter[J].Chinese Journal of Computational Mechanics,2007,24(4):472-476.
[5]赵博宇,丁勇,吴斌.基于扩展卡尔曼估计算法的地震模拟振动台模型识别[J].振动与冲击,2014,33(12):145-150.ZHAO Boyu,DING Yong,WU Bin.Identification of shaking table model for seismic simulation based on an extended Kalman estimator[J].Journal of Vibration and Shock,2014,33(12):145-150.
[6]谢献忠,易伟建.结构物理参数时域识别的子结构方法研究[J].工程力学,2005,22(5):94-98.XIE Xianzhong,YI Weijian.A substructure method for parameter estimation in time domain[J].Engineering Mechanics,2005,22(5):94-98.
[7]穆腾飞,周丽.输入未知条件下基于自适应广义卡尔曼滤波的结构损伤识别[J].振动工程学报,2014,27(6):827-834.MU Tengfei,ZHOU Li.Structural damage identification using adaptive extended Kalman filter with unknown inputs[J].Journal of Vibration Engineering,2014,27(6):827-834.
[8]周东华,胡艳艳.动态系统的故障诊断技术[J].自动化学报,2009,35(6):748-758.ZHOU Donghua,HU Yanyan.Fault diagnosis techniques for dynamic systems[J].Acta Automatica Sinica,2009,35(6):748-758.
[9]CARLSON N A,BERARDUCCI M P.Federated kalman filter simulation result[J].Journal of the Institute of Navigation,1994,41(3):297-321.
[10]卞鸿巍,李安,覃方君.现代信息融合技术在组合导航中的应用[M].北京:国防工业出版社,2010.
[11]戴霖.基于扩展卡尔曼滤波的结构损伤识别方法[D].南昌:南昌大学,2014.
[12]WEBER B,PAULTRE P,PROULX J.Consistent regularization of nonlinear model updating for damage identification[J].Mechanical Systems and Signal Processing,2009,23(6):1965-1985.