Online damage detection via a synergy of proper orthogonal decomposition and recursive Bayesian filters

被引:36
作者
Azam, S. Eftekhar [1 ]
Mariani, S. [2 ]
Attari, N. K. A. [3 ]
机构
[1] Univ Thessaly, Dept Mech Engn, Volos, Greece
[2] Politecn Milan, Dept Civil & Environm Engn, Milan, Italy
[3] BHRC, Dept Struct Engn, Tehran, Iran
关键词
Structural health monitoring (SHM); Reduced-order modeling; Damage detection; Model updating; Kalman filtering; Proper orthogonal decomposition (POD); EXTENDED KALMAN FILTER; ARTIFICIAL NEURAL-NETWORKS; MODEL ORDER REDUCTION; PHYSICAL INTERPRETATION; DOMAIN DECOMPOSITION; IDENTIFICATION; SYSTEMS; TECHNOLOGY; QUANTIFICATION; UNCERTAINTY;
D O I
10.1007/s11071-017-3530-1
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
In this paper, an approach based on the synergistic use of proper orthogonal decomposition and Kalman filtering is proposed for the online health monitoring of damaged structures. The reduced-order model of a structure is obtained during an (offline) initial training stage of monitoring; afterward, effective estimations of a possible structural damage are provided online by tracking the evolution in time of stiffness parameters and projection bases handled in the model order reduction procedure. Such tracking is accomplished via two Kalman filters: a first (extended) one to deal with the time evolution of a joint state vector, gathering the reduced-order state and the stiffness terms degraded by damage; a second one to deal with the update of the reduced-order model in case of damage evolution. Both filters exploit the information conveyed by measurements of the structural response to the external excitations. Results are reported for a (pseudo-experimental) benchmark test on an eight-story shear building. Capability and performance of the proposed approach are assessed in terms of tracked variation of the stiffness terms of the reduced-order model, identified damage location and speed-up of the whole health monitoring procedure.
引用
收藏
页码:1489 / 1511
页数:23
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