Structural damage identification based on proper orthogonal decomposition and Kalman filter

PDF(1980 KB)

Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (18) : 304-312.

YANG Shaochong1, YAO Yuan1, ZHANG Kai1, SONG Kangning1, LIU Jialiang1, JIN Jialin1, MA Lianhua2, FANG Youliang1

Author information +

History +

Abstract

At present, existing damage identification methods are difficult to realize real-time and accurate health monitoring of structures. To solve this problem, a POD-KF method based on Proper Orthogonal Decomposition (POD) and Kalman filter is developed. The structural damage identification method under dynamic loading is studied. The method uses Kalman filter to update the subspace of the reduced order model online to solve the problem of large computation and copy with the difficulty in dynamic analysis of multiple degrees of freedom structures under unknown loads. Only the response of the structure is used to track the evolution of the damage and locate it in real time, without time-consuming retraining phase. The effectiveness of the proposed theory and the proposed algorithm is verified by numerical simulation of the six-story shear frame structure. The results show that the proposed method can not only reduce the amount of calculation, but also guarantee high accuracy, effectively identify the location and severity of damage in the shear frame structure and can effectively suppress the noise in the response signal. At the same time, it still has high precision in dealing with small degree of damage.

Key words

Damage identification / proper orthogonal decomposition (POD) / Kalman filter / data driven / health monitoring

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

YANG Shaochong1, YAO Yuan1, ZHANG Kai1, SONG Kangning1, LIU Jialiang1, JIN Jialin1, MA Lianhua2, FANG Youliang1. Structural damage identification based on proper orthogonal decomposition and Kalman filter[J]. Journal of Vibration and Shock, 2023, 42(18): 304-312

References

[1] KONG X, CAI C, HU J. The state-of-the-art on framework of vibration-based structural damage identification for decision making [J]. Applied Sciences, 2017, 7(5): 497
[2] LI H N, REN L, JIA Z, et al. State-of-the-art in structural health monitoring of large and complex civil infrastructures [J]. Journal of Civil Structural Health Monitoring, 2015, 6(1): 3-16
[3] 赵一男，公茂盛，杨游. 结构损伤识别方法研究综述[J]. 世界地震工程, 2020, 36(02): 73-84.
ZHAO Yinan, GONG Maosheng, YANG You. A review of structural damage identification methods [J]. World Earthquarke Engineering, 2020, 36(02): 73-84.
[4]李慧民，董美美，熊雄，等. 基于振动的结构损伤识别研究综述[J]. 建筑结构, 2021, 51(04): 45-50.
LI Huimin, DONG Meimei, XIONG Xiong, XIONG Deng. State-of-the-art review of vibration-based damage identification framework for structures [J]. Building Structure, 2021, 51(04): 45-50.
[5] RITTO T G, BUEZAS F S, SAMPAIO R. A new measure of efficiency for model reduction: Application to a vibroimpact system [J]. Journal of Sound and Vibration, 2011, 330(9): 1977-1984.
[6] 陈兵，龚春林，仇理宽，等. 基于频域本征正交分解的几何非线性动力学降阶[J]. 振动与冲击, 2020, 39(21): 163-172.
CHEN Bing, GONG Chunlin, QIU Likuan, et al. Order reduction of geometrically nonlinear dynamic system based on POD in frequency domain [J]. Journal of Vibration and Shock, 2020, 39(21): 163-172.
[7] 梅冠华, 康灿, 张家忠. 二维壁板颤振的本征正交分解降阶模型研究[J]. 振动与冲击, 2017, 36(23): 144-151.
MEI Guanhua, KANG Can, ZHANG Jiazhong. Reduced order model based on proper orthogonal decomposition for two- dimensional panel flutter [J]. Journal of Vibration and Shock, 2017, 36(23): 144-151.
[8] 邱亚松, 白俊强, 华俊. 基于本征正交分解和代理模型的流场预测方法[J]. 航空学报, 2013, 34(06): 1249-1260.
QIU Yangsong, BAI Junqiang, HUA Jun. Flow field estimation method based on proper orthogonal decomposition and surrogate model [J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(06): 1249-1260.
[9] XIE D, XU M, DAI H H, et al. Proper orthogonal decomposition method for analysis of nonlinear panel flutter with thermal effects in supersonic flow [J]. Journal of Sound and Vibration, 2015, 337: 263-283.
[10] LU K, JIN Y L, CHEN Y S, et al. Review for order reduction based on proper orthogonal decomposition and outlooks of applications in mechanical systems [J]. Mechanical Systems and Signal Processing, 2019, 123: 264-297.
[11] EFTEKHAR AZAM S, MARIANI S. Investigation of computational and accuracy issues in POD-based reduced order modeling of dynamic structural systems [J]. Engineering Structures, 2013, 54: 150-167.
[12] GALVANETTO U, VIOLARIS G. Numerical investigation of a new damage detection method based on proper orthogonal decomposition [J]. Mechanical Systems and Signal Processing, 2007, 21(3): 1346-1361.
[13] GALVANETTO U, SURACE C, TASSOTTI A. Structural damage detection based on proper orthogonal decomposition: experimental verification [J], AIAA Journal. 2008, 46(7): 1624-1630.
[14] 杨斌, 程军圣. 基于奇异值分解及特征正交分解的结构损伤检测方法[J]. 振动与冲击, 2014, 33(22): 164-167.
YANG Bin, CEHNG Junsheng. Structure damage detection method based on singular value decomposition and proper orthogonal decomposition [J]. Journal of Vibration and Shock, 2014, 33(22): 164-167.
[15] 王丹生, 高智, 杨海萍, 朱宏平. 基于特征正交分解的梁结构损伤识别[J]. 振动与冲击, 2009, 28(11): 122-125.
WANG Dansheng, GAO Zhi, YANG Haiping, ZHU Hongping. Damage identification for beam structures based on proper orthogonal decomposition [J]. Journal of Vibration and Shock, 2009, 28(11): 122-125.
[16] WANG D S, XIANG W, ZENG P, et al. Damage identification in shear-type structures using a proper orthogonal decomposition approach [J]. Journal of Sound and vibration, 2015, 355: 135-149.
[17] CORIGLIANO A, DOSSI M, MARIANI S. Model order reduction and domain decomposition strategies for the solution of the dynamic elastic–plastic structural problem [J]. Computer Methods in Applied Mechanics and Engineering, 2015, 290: 127-155.
[18] 雷鹰, 刘丽君, 郑翥鹏. 结构健康监测若干方法与技术研究进展综述[J]. 厦门大学学报(自然科学版), 2021,60(03):630-640.
LEI Ying, LIU Lijun, ZHENG Zhupeng. Review on the Developments of some methods and techniques in structural health monitoring [J]. Journal of Xiamen University (Natural Science), 2021, 60(03): 630-640.
[19] 黄进鹏, 黄杰忠, 张纯, 等. 基于车桥耦合振动信号和扩展卡尔曼滤波的桥梁结构损伤识别[J]. 土木工程与管理学报, 2018,35(05):140-144.
HUANG Jinpeng, HUANG Jiezhong, ZHANG Chun, SONG Guquan. Bridge damage identification based on vehicle-bridge coupling vibration signals and extended kalman filtering [J]. Journal of Civil Engineering and Management, 2018, 35(05): 140-144.
[20] LIU L J, SU Y, ZHU J J, et al. Data fusion based EKF-UI for real-time simultaneous identification of structure systems and unknown external inputs [J]. Measurement, 2016, 88: 456-467.
[21] LEI Y, CHEN F, ZHOU H. An algorithm based on two-step Kalman filter for intelligent structural damage detection [J]. Structural Controland and Health, 2015, 22: 694-701.
[22] 唐宏志, 姜金辉. 扩展卡尔曼滤波对时变参数追踪性能的影响研究[J]. 南京航空航天大学学报, 2022, 54(02): 304-310.
Tang Hongzhi, Jiang Jinhui. Research on tracking performance of extended kalman filter for time varying parameters [J]. Journal of Nanjing University of Aeronautics and Astronautics, 2022, 54(02): 304-310.