다중 분산점 칼만필터를 이용한 급격한 구조손상 탐지 기법 개발 = Unscented Kalman Filter with Multiple Sigma Points for Robust System Identification of Sudden Structural Damage

다국어 초록 (Multilingual Abstract)

The unscented Kalman filter (UKF), which is widely used to estimate the states of nonlinear dynamic systems, can be improved to realize robust system identification by using multiple sigma-point sets. When using Kalman filter methods for system identification, artificial noises must be appropriately selected to achieve optimal estimation performance. Additionally, an appropriate scaling factor for the sigma-points must be selected to capture the nonlinearity of the state-space model. This study entailed the use of Bouc–Wen hysteresis model to examine the nonlinear behavior of a single-degree-of-freedom oscillator. On the basis of the effects of the selected artificial noises and scaling factor, a new UKF method using multiple sigma-point sets was devised for improved robustness of the estimation over various signal-to-noise-ratio values. The results demonstrate that the proposed method can accurately track nonlinear system states even when the measurement noise levels are high, while being robust to the selection of artificial noise levels.

참고문헌 (Reference)

• 1 Noori, M. N., "Zero and Nonzero Mean Random Vibration Analysis of a New General Hysteresis Model" 1 (1): 192-201, 1986
• 2 Chatzi, E. N., "The Unscented Kalman Filter and Particle Filter Methods for Nonlinear Structural System Identification with Non-Collocated Heterogeneous Sensing Struct" 16 (16): 99-123, 2009
• 3 Van der Merwe, R., "The Square-Root Unscented Kalman Filter for State and Parameter-Estimation" 6 : 3461-3464, 2001
• 4 Fujimura, K., "Tail-Equivalent Linearization Method for Nonlinear Random Vibration" 22 (22): 63-76, 2007
• 5 Wang, C. -H., "Reliability and Redundancy of pre-Northridge Low-Rise Steel Building under Seismic Excitation" Univ. Illinois at Urbana-Champaign 1998
• 6 Lee, S. -H., "Regularization-Based Dual Adaptive Kalman Filter for Identification of Sudden Structural Damage Using Sparse Measurements" 10 (10): 850-, 2019
• 7 Baber, T. T., "Random Vibration of Pinching Hysteretic Systems" 110 (110): 1036-1049, 1984
• 8 Baber, T. T., "Random Vibration Hysteretic, Degrading Systems" 107 (107): 1069-1087, 1981
• 9 Yi, S. -R., "Particle Filter Based Monitoring and Prediction of Spatiotemporal Corrosion Using Successive Measurements of Structural Responses" 18 (18): 3909-, 2018
• 10 Song, J., "Generalized Bouc-Wen Model for Highly Asymmetric Hysteresis" 123 (123): 610-618, 2006
• 11 Foliente, G. C., "Equivalent Linearization of Generally Pinching Hysteretic, Degrading Systems" 25 (25): 611-629, 1996
• 12 Thurin, J., "Ensemble-based Uncertainty Estimation in Full Waveform Inversion" 219 (219): 1613-1635, 2019
• 13 Yi, S. -R., "Bivariate Gaussian Mixturebased Equivalent Linearization Method for Stochastic Seismic Analysis of Nonlinear Structures" 47 (47): 678-696, 2017
• 14 Candy, J. V., "Bayesian Signal Process: Classical, Modern, and Particle Filtering Methods" John Wiley & Sons, Inc 2006
• 15 Nguyen, V. H., "Application of the Unscented Kalman Filter to Estimate the Material Properties of a Layered Half-Space" 15 (15): 2350005-, 2022
• 16 Papakonstantinou, K. G., "A Scaled Spherical Simplex Filter (S3F) with a Decreased n + 2 Sigma Points Set Size and Equivalent 2n + 1 Unscented Kalman (UKF) Accuracy" 163 (163): 107433-, 2020
• 17 Kalman, R. E., "A New Approach to Linear Filtering and Prediction Problems" 82 (82): 35-45, 1960
• 18 Vaiana, N., "A Class of Uniaxial Phenomenological Models for Simulating Hysteretic Phenomena in Rate-Independent Mechanical Systems and Materials" 93 (93): 1647-1669, 2018