Back to Search
Start Over
Reliable Detection of Induction Motor Rotor Faults Under the Rotor Axial Air Duct Influence
- Publication Year :
- 2014
-
Abstract
- © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” Upon publication, authors are asked to include either a link to the abstract of the published article in IEEE Xplore®, or the article’s Digital Object Identifier (DOI).<br />Axial cooling air ducts in the rotor of large induction motors are known to produce magnetic asymmetry and can cause steady-state current or vibration spectrum analysis based fault detection techniques to fail. If the number of axial air ducts and that of poles are identical, frequency components that overlap with that of rotor faults can be produced for healthy motors. False positive rotor fault indication due to axial ducts is a common problem in the field that results in unnecessary maintenance cost. However, there is currently no known test method available for distinguishing rotor faults and false indications due to axial ducts other than offline rotor inspection or testing. Considering that there is no magnetic asymmetry under high slip conditions due to limited flux penetration into the rotor yoke, the detection of broken bars under the start-up transient is investigated in this paper. A wavelet-based detection method is proposed and verified on custom-built lab motors and 6.6-kV motors misdiagnosed with broken bars via steady-state spectrum analysis. It is shown that the proposed method provides the reliable detection of broken bars under the start-up transient independent of axial duct influence.
Details
- Database :
- OAIster
- Notes :
- TEXT, English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1138430396
- Document Type :
- Electronic Resource