Real-time Structural Displacement Estimation by Fusing Acceleration and Displacement Data with Adaptive Kalman Filter

被引:0
|
作者
Zeng J. [2 ]
Shi Y. [1 ,2 ]
Dai K. [1 ,2 ]
Liao G. [2 ]
机构
[1] Key Lab. of Deep Underground Sci. and Eng. for Ministry of Education, College of Architecture and Environment, Sichuan Univ., Chengdu
[2] College of Architecture and Environment, Sichuan Univ., Chengdu
关键词
adaptive filtering; data fusion; displacement measurement; Kalman filters; structural health monitoring;
D O I
10.15961/j.jsuese.202200013
中图分类号
学科分类号
摘要
Real-time high-precision displacement measurement is important for the safety and life-cycle assessment of engineering structures. To improve the accuracy and stability of displacement measurement based on Global Navigation Satellite System (GNSS) technology, an adaptive multi-rate Kalman filter is proposed to fuse the acceleration and displacement data. Due to unreasonable settings of noise parameters, the accuracy of displacement estimation can be seriously degraded. By utilizing the characteristics of acceleration and displacement measurement noises, the adaptive estimation is realized through estimating the variance of their corresponding noises separately. Considering the noise characteristics of accelerometer and GNSS device, the estimation of noise parameters in the adaptive filter is simplified to estimate only the variance of displacement noise. The Sage-Husa estimator is used to realize the adaptive estimation of displacement noise variance so that the filter can reach a stable real-time displacement estimation under inaccurate noise parameters. First, the settings of initial noise parameters in the proposed adaptive filter are discussed to determine its rule. Then the displacement estimation performance of the filter at different signal frequencies is discussed through the harmonic displacement under time-invariant noise and time-varying noise. Finally, the effectiveness of the proposed technique is demonstrated by using a numerical simulation response from a 1.5 MW wind turbine tower under wind-earthquake coupling. The results show that even if the initial noise parameters are inaccurate and the displacement measurement noise is time-varying, the proposed technique still has satisfactory performance and robustness in real-time estimation. This research can provide a reference for real-time and high-precision displacement monitoring of structures. © 2023 Editorial Department of Journal of Sichuan University. All rights reserved.
引用
收藏
页码:188 / 196
页数:8
相关论文
共 28 条
  • [1] Kim K, Sohn H., Dynamic displacement estimation by fusing LDV and LiDAR measurements via smoothing based Kalman filtering[J], Mechanical Systems and Signal Processing, 82, pp. 339-355, (2017)
  • [2] Kim J, Kim K, Sohn H., Autonomous dynamic displacement estimation from data fusion of acceleration and intermittent displacement measurements[J], Mechanical Systems and Signal Processing, 42, pp. 194-205, (2014)
  • [3] Wujiao Dai, Xixiu Wu, Feixue Luo, Integration of GPS and accelerometer for high building vibration monitoring[J], Journal of Vibration and Shock, 30, 7, pp. 223-226, (2011)
  • [4] Jiayong Yu, Shao Xudong, Meng Xiaolin, Et al., Experimental research on dynamic monitoring of bridges using GNSS and accelerometer[J], China Journal of Highway and Transport, 27, 2, pp. 62-69, (2014)
  • [5] Kim K, Choi J, Chung J, Et al., Structural displacement estimation through multi-rate fusion of accelerometer and RTK-GPS displacement and velocity measurements[J], Measurement, 130, pp. 223-235, (2018)
  • [6] Hwang J, Yun H, Park S K, Et al., Optimal methods of RTK-GPS/accelerometer integration to monitor the displacement of structures[J], Sensors(Basel), 12, 1, pp. 1014-1034, (2012)
  • [7] Hongping Zhu, Ke Gao, Yong Xia, Et al., Multi-rate data fusion for dynamic displacement measurement of beam-like supertall structures using acceleration and strain sensors[J], Structural Health Monitoring, 19, 2, pp. 520-536, (2020)
  • [8] Hong Y H, Se gun Lee, Lee H S., Design of the FEM-FIR filter for displacement reconstruction using accelerations and displacements measured at different sampling rates[J], Mechanical Systems and Signal Processing, 38, 2, pp. 460-481, (2013)
  • [9] Jiayong Yu, Xiaolin Meng, Xudong Shao, Et al., Identification of dynamic displacements and modal frequencies of a medium-span suspension bridge using multimode GNSS processing[J], Engineering Structures, 81, pp. 432-443, (2014)
  • [10] Jiayong Yu, Xiaolin Meng, Banfu Yan, Et al., Global Navigation Satellite System-based positioning technology for structural health monitoring:A review[J], Structural Control and Health Monitoring, 27, 1, pp. 1-27, (2020)