Abstract：It’s found from the literature that the discrete-coordinated systems following the FE technique are generally employed by almost all works for investigating the optimal sensor placement problem. Although the discrete-coordinated systems can provide a convenient and practical approach to the dynamic response analysis of arbitrary structures, the solutions obtained can only approximate their actual dynamic behavior. This will affects the results of optimal sensor placement, and the sensors are confined to be placed at only a very limited number of discrete locations. For the distributed-parameter system, the sensor locations are identified by their coordinates that are continuous within the region of interest. In such situation, the continuous-coordinated models are more suitable than the FE-based discrete-coordinated models for investigating the optimal sensor placement problems. For the purpose of structural model updating, this paper is intended to develop a sensor/actuator configuration methodology for the distributed-parameter system based on both the Bayesian statistical system identification method and information entropy. In the proposed methodology, the Bayesian statistical system identification method is firstly adopted to identify the optimal values and associated uncertainties of the structural modeling parameters, wherein the information entropy is employed as a scalar measure to quantify the uncertainty of the identified structural modeling parameters. Then, the problem of optimal sensor/actuator placement is formulated as a continuous optimization problem, in which the information entropy is minimized by using the genetic algorithm, with the sensor/actuator configurations as the minimization variables. Maximum amount of information about the structural modeling parameters would be obtained if the sensors/actuators are placed at their optimal locations, indicating the uncertainties of identified modeling parameters being minimum. The proposed methodology is verified through a set of numerical simulation cases for a three-span continuous bridge model with two elastic supports at top of piers.
尹 涛. 一种基于信息熵的分布参数结构传感器/激励器优化布置方法[J]. , 2014, 33(22): 51-57.
Yin Tao. A probabilistic approach for optimal sensor/actuator configuration of distributed-parameter systems based on information entropy. , 2014, 33(22): 51-57.
2014, Vol. 33 
