A nonlinear cointegration approach with applications to structural health monitoring

13th International Conference on Motion and Vibration Control; 12th International Conference on Recent Advances in Structural Dynamics

Shi, H.^1 ; Worden, K.^1 ; Cross, E.J.^1

Dynamic Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield

S1 3JD, United Kingdom^1

关键词: Environmental change;    Environmental variability;    Gaussian process regression;    Nonlinear cointegration;    Nonlinear relations;    Structural damages;    Structural degradation;    Structural health monitoring (SHM);   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/744/1/012025/pdf
DOI  :  10.1088/1742-6596/744/1/012025

来源: IOP

【 摘 要 】

One major obstacle to the implementation of structural health monitoring (SHM) is the effect of operational and environmental variabilities, which may corrupt the signal of structural degradation. Recently, an approach inspired from the community of econometrics, called cointegration, has been employed to eliminate the adverse influence from operational and environmental changes and still maintain sensitivity to structural damage. However, the linear nature of cointegration may limit its application when confronting nonlinear relations between system responses. This paper proposes a nonlinear cointegration method based on Gaussian process regression (GPR); the method is constructed under the Engle-Granger framework, and tests for unit root processes are conducted both before and after the GPR is applied. The proposed approach is examined with real engineering data from the monitoring of the Z24 Bridge.