Optimal locations of a multi-type sensor system for Structural health monitoring

The installation of comprehensive structural health monitoring systems (SHMS) to important structures has become a trend to assure the safety of structures during their long service life. The operational performance of SHMS strongly relies on the properties of the sensor system such as the type, number and spatial location of the sensors in the structure. Meanwhile, various types of sensors are now available to measure not only the global response but also the local response of the structure. The number of sensors is always limited compared with a large-scale structure; therefore multi-type sensor location selection and data fusion technique become a challenging yet critical issue in the implementation of an effective SHMS. This paper addresses a practical and challenging problem of placing multi-type sensors in a structure including strain gauges displacement transducers, and accelerometers, which are widely used in SHMS. The number and locations of the three type sensors are determined with the aim of minimizing the estimation error of unobserved structural responses based on incomplete measurement in which the Kalman filter algorithm is utilized. By minimizing the overall estimation errors at the locations of interest and reducing estimation errors to a desired target level, the initial set of candidate sensor locations is reduced to a smaller optimal set. An experimental study is used to illustrate the effectiveness and accuracy of the presented multi-type sensor location selection approach. The experimental results indicate that the proposed approach offers an effective way to design a multi-type sensor system and the optimal sensor locations can produce sufficient information on the response estimation.