Adaptive Measurement for Automated Field Reconstruction and Calibration of Magnetic Systems

This paper presents an efficient method to adaptively determine the locations for taking measurements for automated calibration of electromagnetic systems using reconstructed magnetic fields. This coupled measurement- computation method solves the Laplace's equation with measured boundary conditions. Along with the formulation of two selection criteria (chord-height and data-spacing), an adaptive scanning algorithm has been developed, which bases four local measurements to determine the next measurement point. This adaptive method, which relaxes the assumption of approximately known structure, has been illustrated (with experimental verification) with three practical applications; electromagnetic velocity probe, electromagnetic flow-meter (EMF) and spherical motor. Comparisons against published data demonstrate that the adaptive measurement algorithm greatly reduces the number of measurements and shortens the scanning route length of all three applications without sacrificing the accuracy of the computed results. Dry calibration results of an EMF were also experimentally verified against test data obtained from a standard flow-rig confirming that a relative error of 0.2% can be achieved. This finding makes the cost-effective dry calibration a practical alternative to the conventional flow rig calibration. As demonstrated on a spherical motor, the flexibility to include a least-square curve fit offers a practical means to filter measurement noise.