Abstract
Load identification plays an important role in structural health monitoring, which aims at preventing structural failures. In order to identify load for linear systems and nonlinear systems, this paper presents methods to identify load for a cantilever beam based on dynamic strain measurement by Fiber Bragg Grating (FBG) sensors. For linear systems, the proposed inverse method consists of Kalman filter with no load terms and a linear estimator. For nonlinear systems, the proposed inverse method consists of cubature Kalman filter (CKF) with no load terms and a nonlinear estimator. In the process of load identification, the state equations of the beam structures are constructed by using the finite element method (FEM). Kalman filter or CKF is used to suppress noise. The residual innovation sequences, gain matrix, and innovation covariance generated by Kalman filter or CKF are used to identify a load. To prove the effectiveness of the proposed method, numerical simulations and experiments of the beam structures are employed and the results show that the method has an excellent performance.
Highlights
Load identification is an important research area in structural health monitoring [1,2,3,4].For reliability and cost effectiveness in the design and analysis of structures, accurate identification of the location and magnitude of a load is desirable
To verify the accuracy of the identification method, the simulations and experiments of a linear system and a nonlinear system are employed, and the results show that the system based on Fiber Bragg Grating (FBG) sensors has an excellent performance
The based on a Kalman filter, and this can be helpful to control the beam structure by using optimal proposed method is based on a Kalman filter, and this can be helpful to control the beam control theory after identifying thetheory load. after identifying the load
Summary
Load identification is an important research area in structural health monitoring [1,2,3,4]. The system of load identification needs to stall many sensors, which is difficult to realize in engineering applications To amend these flaws, this work focuses on identifying a load by applying FBG sensors. For Lin’s work, turning nonlinear systems to linear systems with EKF can cause errors, and may result in sub-optimal performance and divergence when dealing with strong nonlinear systems To amend this flaw, this work proposes a new method that is based on CKF and a nonlinear estimator to identify a load for nonlinear systems. To verify the accuracy of the identification method, the simulations and experiments of a linear system and a nonlinear system are employed, and the results show that the system based on FBG sensors has an excellent performance
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