In a high temperature environment, it is challenging to perform structural health monitoring (SHM), which has become a required task for many important civil structures in harsh environments. A SHM system in high temperature environments requires a large number of sensors for different data resource measurements, for example, strain and temperature. The accuracy of the measurement is highly dependent on the trade-off between the number of sensors of each type and the associated cost of the system. This paper introduces a sensor optimization approach based on an evolutionary strategy for the multi-objective sensor placement of structural health monitoring in high temperature environments. A single-bay steel frame with localized high temperature environment validates the multi-objective function of the evolutionary strategy. The variance between the theoretical and the experimental analysis was within 5 %, indicating an effective sensor placement optimization using the developed genetic algorithm, which can be further applied to general sensor optimization for SHM system applications in high temperature environments.
A magnetic and inertial measurement unit (MIMU) usually measures acceleration, rotation rate, and earth's magnetic field in order to determine a body's attitude. In order to find the orientation information using all sensors a fusion algorithm is used. This paper compares two approaches used for a Attitude and Heading Reference System (AHRS), namely Madgwick and Mahony with a basic fusion approach. Foot mounted MIMU data is used to estimate the Euler angles as well as the position. The results show that Madgwick obtains better heading orientation than Mahony and the basic AHRS approach in terms of the error (RMSE) of the Euler angles when compared to the ground truth. However, the execution time of Mahony is less than Madgwick with the basic AHRS taking the longest.
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