Study Of Data Fusion Method For Structural Health Monitoring Information

Because of the real-time features and complexity in structural health monitoring system, study of the method for dealing with the structural health monitoring information is important to improve accuracy of data. In the paper, discrete Kalman Filter and distributed dynamic data fusion technology is used to dispose the information from structural health monitoring. The theory analysis and numerical simulation shows that multi-sensor data fusion algorithm can improve data accuracy effectively.The state equation of structural system is built basing on its dynamical equation. State equation commonly is continuous-time system equation. In the engineering measure discrete-time data is abtained. Given the process to disperse continuous-time system and parameters corresponded to the discrete-time system.Using the discrete Kalman Filter and distributed dynamic data fusion technology, the paper describes the methodology to provide full state and more accurate information according to the structural health monitoring information. Firstly, the formulas to build a discrete time system are drawn, from the continuous time system, then the full state information is obtained with the separated estimation of each sensor using Kalman Filter. After that, the distributed dynamic data fusion technology is applied to approach the more accurate information. The mathematical model for a five-layer frame structure is built, with which the algorithm in this paper is verified. Due to the numerical experiment results and the error analysis results, the beneficial conclusions for engineering application are also obtained. Finally, the identified error of model parameter and load excitation is considered. The research on the infection of the error indicates that the data accuracy is also improved. It also shows that the data fusion is not sensitive to the identified error of load excitation when the identified error of model parameter is not large. These conclusions show the application value of the algorithm in this paper.