An enhanced sequential sensor optimization scheme and its application in the system identification of a rail-sleeper-ballast system

The problem of optimal sensor placement for system identification and damage detection is addressed by the development of a robust method based on Bayesian theory. Information entropy is used as the optimality measure to select the optimal configuration from the candidate configurations for a given number of sensors. The enhanced sequential sensor placement (ESSP) algorithm was developed to efficiently address the computational bottlenecks that may arise when a large number of measurable degrees of freedom (DOFs) are considered for candidate configurations. In this paper, the sensor redundancy problem in finely meshed models was addressed by considering (1) the spatial correction of prediction errors at measurable DOFs and (2) a minimum sensor interval. The proposed ESSP algorithm and the two strategies for handling sensor redundancy were studied by comparing the optimal configurations from the conventional methods and that from the ESSP algorithm for a rail-sleeper-ballast system. Finally, the optimal sensor configurations thus obtained were verified via model updating of an in-situ ballasted track system using measured data from an impact hammer test. The analysis results clearly show improvements in the optimality of the sensor configuration with the proposed method relative to the conventional methods.