Abstract

The present paper proposes a refined nonlinear axial bearing model not too complex for real–time applications, which may potentially improve the estimator quality of modulation–based self–sensing. Measurements show that the AC component (ripple) of the bearing current is an underlinear function of the AC excitation voltage, which causes the bearing admittance amplitude |Y (jω, iac)| to considerably increase at low amplitudes of iac. The non– consideration of this effect might explain the poor dynamic performance observed in former implementations of modulation–based self–sensing. The model proposed in this paper includes a dynamic (rate dependent) hysteresis model of the Bertotti type, where the magnetic H field is a nonlinear function of the time derivative of flux density dB/dt. Eddy currents are modelled by a linear lumped RL ladder network (Cauer type). The proposed model gives encouraging results and is able to reproduce some of the observed phenomena. INTRODUCTION Self–sensing permits active magnetic bearings to dispense with dedicated position sensors and, instead, reconstruct rotor position information from the voltage and current signals of the actuator coils. Thus, the hardware amount in the machine environment and the amount of cabling can be reduced, which potentially increases hardware reliability. Although self–sensing technology has progressed in terms of theoretical understanding [1, 2] and by the launching of commercial applications, some unsolved technical challenges impede the application of self–sensing to a broader range of applications. One of these technical challenges concerns self–sensing for non–laminated axial bearings. Non–laminated thrust bearings are a difficult candidate for self– sensing because of both the presence of strong eddy current effects and magnetic material nonlinearity. FIGURE 1: Magnetic Bearing Test Rig from MECOS. FIGURE 2: Flux density obtained by linear FEM simulation (COMSOL) @20 Hz, with μr = 5′000 and σ = 2 · 10 S/m.

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