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Magnetic positioning technology is a novel and direct method to measure the attitude and position information of discrete particles in dense granular flow. However, owing to the need to manually set the initial value and other boundary conditions in the process of algorithm solution, the deviation between the measured results and actual values cannot be ignored. To solve this problem, a hybrid optimization algorithm of particle swarm optimization algorithm and sequential quadratic programming algorithm (PSO-SQP) is proposed in this paper, which combines the advantages of PSO algorithm being insensitive to initial value with the advantages of SQP algorithm being fast and accurate to solve magnetic positioning parameters. The attitude calculated by magnetic positioning and calculated by inertial measurement are fused together through Kalman filter to accurately measure the attitude of discrete particles. The static experiments show that PSO-SQP can solve the position without setting the initial value. Meanwhile, the attitude accuracy is improved by data fusion. Finally, the method is applied to measure the motion of discrete particles in a 3D silo, and it is observed that the method can accurately obtain the information of the spatial position, translational motion, and rotational motion of the discrete particles in the silo. The experimental results reveal the laws of motion of granular flow in the silo, and provide data reference for improving the storage and transportation of particle materials in industrial production process.

3D Measurement of Particle Movement in a Silo Using Magnetic Positioning and Inertial Navigation Technologies