Displacement Measurement Based on Data Fusion and Real-Time Computing

Abstract

Displacement due to the deformation of civil structures such as bridges and buildings (caused by different external loads: e.g., vehicle, wind, and temperature) is an important factor for structure safety evaluation. The effectiveness of structure maintenance is highly dependent on the accuracy and reliability of the structural displacement measurement. An accelerometer is a tool that indirectly measures displacement and has gained widespread interest. However, the accuracy is questionable because noise exists in acceleration measurements and also can be undermined by accumulated errors during the double integration of the acceleration process. In this paper, a new displacement measurement algorithm based on data fusion technique was studied. In this algorithm, the Kalman filter was used as the fusion technique. The acceleration was taken as the primary measurement, and rotation measurement was used as the second measurement to minimize the effect of the error in the displacement prediction during the double integration of the acceleration process. To validate the performance and reliability of the algorithm, two lab tests were conducted: cantilever beam test and simply supported beam test. The results show that the proposed algorithm could minimize the effect of the accumulated error on the double integration of acceleration, thus providing a reliable estimation of the vertical displacement of a cantilever beam and simply supported beam.