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Highlights • Method simulates effects of launch conditions on rail-gun current and armature speed. • The input five parameters are closely related to the rail-gun launchment. • Three neural networks predict the rail-gun current and armature speed with low error. • General Regression neural network is optimal on error, time cost, parameter design. Abstract In this paper, based on the advantages of artificial neural network, such as good tolerance of data noise, strong ability of nonlinear mapping, multi-dimensional input variables, fast operation, low error, etc., a method of using artificial neural network for data prediction is proposed for the research of rail-gun. The results show that it is feasible to use the Back Propagation Neural Network, the Radial Basis Function Neural Network and the General Regression Neural Network to realize the method of prediction and simulation of the rail-gun current and the armature speed curve through relevant parameters. The General Regression Neural Network has superiority in error performance and time cost of neural network training and simulation. [ABSTRACT FROM AUTHOR]

An axial inductive displacement sensor for magnetic bearings is proposed in this paper. The proposed sensor can be fixed on radial direction to measure axial displacement of the suspended armature, showing the potential to integrate with a radial inductive sensor. The proposed sensor shows the potential to be used in some fields where eddy current sensors have already been applied. [Display omitted] • Proposes an novel axial inductive sensor. • Builds theory model of 3D inductance. • Shows the potential in more displacement measurement fields. An axial inductive displacement sensor, which can be fixed on radial direction to measure axial displacement of the suspended rotor, is proposed in this paper. Based on Schwarz-Christoffel transformation, 3D inductance of the proposed sensor is calculated. The novel approach for 3D inductance calculation is compared with 3D FEM results, showing the feasibility for practical applications. Static and dynamic characteristics are evaluated on a magnetic bearing platform. Sensitivity and linearity are 2.63 V/mm and 0.46%, respectively. Static noise result shows that the proposed sensor can reach a resolution of 5 μm. Dynamic characteristics tests, including vibrating and shocking experiments show that the displacement detection capability of the proposed sensor is similar to that of an eddy current sensor, showing the potential of the proposed sensor used in other fields where eddy current sensors have already be applied. [ABSTRACT FROM AUTHOR]