Fault diagnosis of active magnetic bearings

Abstract

Active magnetic bearings (AMBs) are intrinsically unstable systems and require feedback control to ensure stable operation. Further, sensors, actuators, and the rotor need to operate under normal conditions, and a fault detection and diagnostics system is necessary to ensure a safe and reliable operation. Accordingly, several studies have developed methods to detect failures associated with the rotor or the electrical system (i.e., AMB). However, prior identification of the dynamic system parameters or the magnetic forces is usually desired, which can be impractical for real machines. To overcome this problem, this study proposes a failure detection method based on a mathematical model and the correlation between the measured states related to the rotor and the control. Artificial neural networks are used to map the states that cannot be measured, and faults are determined by comparing the output correlations of neural networks. Faults in the AMB/rotor system are identified considering various rotor unbalance configurations (mechanical failures) and failures in the position sensor gain and in the magnetic actuator current (electrical failures). Various fault configurations were explored for each case cited. A comparison of the theoretical and experimental results showed good agreement, which demonstrates the adequacy of the method in detecting mechanical and electrical failures in industrial machines.