The National Research Council of Canada has developed Structural Health Monitoring (SHM) test platforms for load and damage monitoring, sensor system testing and validation. One of the SHM platform consists of two 2.25 meter long, simple cantilever aluminium beams that provide a perfect scenario for evaluating the capability of a load monitoring system to measure bending, torsion and shear loads. In addition to static and quasi-static loading procedures, these structures can be fatigue loaded using a realistic aircraft usage spectrum while SHM and load monitoring systems are assessed for their performance and accuracy. In this study, Micro-Electro-Mechanical Systems (MEMS), consisting of triads of gyroscopes, accelerometers and magnetometers, were used to compute changes in angles at discrete stations along the structure. A Least Squares based algorithm was developed for polynomial fitting of the different data obtained from the MEMS installed in several spatial locations of the structure. The angles obtained from the MEMS sensors were fitted with a second, third and/or fourth order degree polynomial surface, enabling the calculation of displacements at every point. The use of a novel Kalman filter architecture was evaluated for an accurate angle and subsequent displacement estimation. The outputs of the newly developed algorithms were then compared to the displacements obtained from the Linear Variable Displacement Transducers (LVDT) connected to the structures. The determination of the best Least Squares based polynomial fit order enabled the application of derivative operators with enough accuracy to permit the calculation of strains along the structure. The calculated strain values were subsequently compared to the measurements obtained from reference strain gauges installed at different locations on the structure. This new approach for load monitoring was able to provide accurate estimates of applied strains and loads.
Wash Testing of Electronic Yarn
