| Summary: | Active magnetic bearings (AMBs) use position feedback to actively control the forces generated by electromagnetic transducers, in order to realise stable suspension of a levitated object. The AMB concept is not new and since its introduction to industry, its application has grown extensively. Although they pose a number of novel qualities rendering them invaluable machine components in modern day industry, the technology has not yet reached its full potential. In the ongoing drive for even wider acceptance and application of AMB technology in industry, efforts with regards to system optimisation as a whole and component integration, are underway to make AMBs more reliable and economical.
Component integration impacts both cost and reliability and one area of research addressing this issue is self-sensing. Self-sensing is the concept where the actuation and sensing functions are realised with a single electromagnetic transducer. In the magnetic bearing the coil current and voltage waveforms are monitored and used to extract the rotor position information. Self-sensing poses a number of advantages over dedicated sensors and has the potential to realise major cost savings. Although self-sensing is not a new concept and the topic has been researched in the past, it remains a challenge. Self-sensing performance is degraded due to problems such as magnetic cross-coupling, eddy currents, saturation and high losses, to name but a few.
The focus of this thesis is on the development of an improved model for self-sensing heteropolar AMBs. The model must also be incorporated into an appropriate self-sensing scheme to demonstrate its ability to address the issues of saturation and magnetic cross-coupling.
In the thesis the amplitude modulation approach using the switching amplifier ripple as high frequency source is adopted. A coupled reluctance network model (RNM) is developed which models the coil impedance at the switching frequency. The model is refined and incorporated into a multiple input multiple output (MIMO) parameter estimation scheme to demonstrate its ability to overcome the aforementioned problems.
An analytical MATLAB®-based RNM is established from literature and refined with the help of finite element method (FEM) models and experimental measurements. Results obtained from the 40 node RNM were shown to closely correlate with results generated by a FEM model with 80,000 nodes. The fact that RNMs are much faster to solve than their FEM counterparts and their ability to precisely map the magnetic behaviour of magnetic bearings, render them the preferred option for online implementation in a self-sensing scheme.
The proposed self-sensing scheme is evaluated in a simulation environment which utilises a transient simulation model (TSM), incorporating important aspects that influence self-sensing performance, i.e. eddy currents, magnetic cross-coupling and hysteresis. Results show that it is possible to address saturation and magnetic cross-coupling with the RNM incorporated into a MIMO parameter estimation scheme. System sensitivity levels achieved, are satisfactory for long term operation. This demonstrates the viability of the proposed self-sensing scheme. === Thesis (Ph.D. (Electrical and Electronic Engineering))--North-West University, Potchefstroom Campus, 2008.
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