Virtual Sensing for Structural Health Monitoring of Off-shore Structures

Abstract

Offshore structures are generally subjected to harsh environment with strong tidal current and wind loading, which demands robust and reliable Structural Health Monitoring (SHM) to avoid any catastrophic failure. The growing size, complexity, and harsh environment of offshore structures lead to difficulties in sensor deployment and maintenance. Response at critical locations in complex offshore rigs are inaccessible during sensor deployment. Moreover, their operational environment demands frequent sensor maintenance for uninterrupted monitoring. Virtual sensing addresses these issues by estimating unmeasured responses with the help of measured responses. This dissertation delineates a virtual sensing method based on Kalman state estimator to combine multi-sensor data under non-stationary random excitation. The estimation algorithm effectively uses the FE model of a structure to predict and fuse different type of structural response (acceleration, strain, and angular displacement). This study investigates various combinations of sensor fusion to improve the estimation accuracy. In addition, an erroneous model is purposefully used to support the robustness and practicality of estimator. The performance of virtual sensing is successfully verified with numerical and experimental test over simply-supported and bottom fixed off-shore structure. Test results conclude that the unmeasured responses are reasonably recovered form measured responses.