Stator current spectral analysis techniques are usually used to detect rotor faults in induction machines. Magnetic field anomalies in the airgap due to the rotor faults result in characteristic side-band harmonic components in the stator current spectrum, which can be measured as rotor fault signatures. A position-varying load torque oscillation at multiples of the rotational speed, however, has exactly the same effect. Stator current harmonics due to a load torque oscillation often obscure and even overwhelm rotor eccentricity fault detection since the magnitude of load oscillation induced harmonics is usually much larger.
Although previous research has suggested some methods to differentiate between these two effects, most of them rely heavily on the accurate estimation of motor parameters. The objective of this research is to develop a far more practical and computationally efficient method to detect rotor faults effectively in the presence of a load torque oscillation. A significant advantage of the proposed scheme is that it does not need any knowledge of motor parameters. The normalized negative sequence information induced by a mixed rotor eccentricity in the stator current or terminal voltage space vector spectra, serves as a reliable rotor fault indicator to eliminate load oscillation effects.
Detailed airgap magnetic field analysis for an eccentric motor is performed and all machine inductance matrices as well as their derivatives are reformulated accordingly. Careful observation of these inductance matrices provides a fundamental understanding of motor operation characteristics under a fault condition. Simulation results based on both induction motor dynamic model and Maxwell 2D Finite Element Model demonstrate clearly the existence of the predicted rotor fault indicator. Extensive experimental results also validate the effectiveness and feasibility of the proposed detection scheme.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/14545 |
| Date | 15 December 2006 |
| Creators | Wu, Long |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
Page generated in 0.0018 seconds