Surfaces Reconstruction Via Inertial Sensors for Monitoring

International audienceThis document deals with the new capabilities of monitoring via the surface reconstruction of stuctures with sensors' arrays systems. Indeed, we will detail here our new demonstrator composed of a smart textile equipped with inertial sensors and a set of processings allowing to reconstruct the shape of the textile moving along time. We show here how this new tool can provide very useful information from the structures

Quantitative Shape Measurement of An Inflatable Rubber Dam Using Inertial Sensors

Shape measurement is of great importance for the effective control and safe operation of inflatable rubber dams. This paper presents for the first time a method to measure the cross-sectional shape of a rubber dam by placing an array of inertial measurement units (IMUs) on the peripheral of the rubber dam. The IMU array measures tangent angles of the dam peripheral by fusing accelerometer and gyroscope measurements. A continuous tangent angle function is derived by interpolating the tangent angles at discrete locations using a cubic spline. Finally, the shape is reconstructed by integrating the tangent angle function along the peripheral of the rubber dam. The performance of the measurement system is validated against a camera on a purpose-built test rig. Experimental results show that the measured and reference shapes are very similar, with a maximum similarity index of 8.5% under typical conditions. In addition, it is demonstrated that the system is robust against node failure by excluding readings of faulty nodes from shape reconstruction

Quantitative Shape Measurement of an Inflatable Rubber Dam Using an Array of Inertial Measurement Units

Shape measurement plays an important role in the condition monitoring and operation control of inflatable rubber dams. This paper presents a method to measure the cross-sectional shape of a rubber dam using an array of inertial measurement units (IMUs) placed on the circumference of the dam. Accelerometer and gyroscope measurements are combined using an adaptive complementary filter to determine the tangent angles of the dam circumference. The adaptive complementary filter adjusts the weights of the accelerometer and gyroscope measurements dynamically in order to reduce the uncertainty in orientation estimation due to external acceleration under dynamic conditions. A natural cubic spline that interpolates the measured tangent angles at discrete locations is used to represent the tangent angles along the dam circumference as a continuous function of the arc length. Finally, the cross-sectional shape is reconstructed by integrating the continuous tangent angle function along the circumference of the dam. Experimental assessment of the measurement system was performed on a purpose-built test rig using a digital camera as a reference measuring device. Results under a typical static condition show that the measured and reference shapes agree well with each other, with a similarity index of 3.74%, mismatch distance of the last IMU node being 12.3 mm and relative error of height measurement being -2.44%. Under dynamic conditions, the measurement results deteriorate due to external acceleration, but considerable improvement is achieved in comparison with an accelerometer-only approach. In addition, elimination of faulty nodes from shape reconstruction has negligible influence on the results, suggesting that the measurement system enjoys a high degree of fault tolerance

Curve Reconstruction via a Ribbon of Sensors

International audienceThis paper presents a novel method for reconstructing curves relying on tangential data which are provided by embedded sensors. The reconstruction process is based on the knowledge of the distribution of the sensors along the curve, represented by a ribbon, and on the associated tangential orientation measurements without any information about their positioning in space, so that this problem is not an envelope problem. We first show how we can obtain these data from sensors and the prototypes we have created. Then we provide methods for planar curves, then for spatial curves and we analyze results with physical sense and convergence in order to validate these methods. Finally, we show some results from both simulated data and real data