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Performance analysis of a 3-phase induction machine with inclined static electricity

Fault diagnosis is gaining more attention for electric machines running critical loads, whose sudden breakdown can result in unpredictable revenue losses. Consequently the motor drive systems with fault diagnostic and prediction features are of great concern and are becoming almost indispensable. Among all kinds of common faults, quite a few have relationship with unequal air-gap. So far, work on detection of eccentricity related faults in synchronous and induction machines have been well documented. However, few are reported on faults resulting fiom axial non-uniform air-gap. This thesis investigates the performance of a three-phase induction machine with non-uniform static eccentricity along axial direction or inclined static eccentricity. A variant of Modified Winding Function Approach (MWFA) is applied to study this fault. The relationship between the number of rotor bars, poles and the existence of fault related current harmonics is discussed. It is shown that inclined eccentricity also demonstrates similar characteristics as circumferential non-uniform air-gap (Static Eccentricity or Dynamic Eccentricity). The case that demands special attention is inclined eccentricity symmetric to the mid-point of machine shaft, which cannot be detected directly from current spectrum. These results will be useful reference to the designers of online tools for machine condition monitoring. Finite Element results to verify the inductance values used in simulation are also presented. The analysis is supplemented by the stator current spectra obtained fkom simulated results for different load and fault conditions. Finally a four-pole, 45 rotor bar, 2 kW induction motor is used to validate the theoretical and simulation results experimentally.

  1. http://hdl.handle.net/1828/590
Identiferoai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/590
Date10 April 2008
CreatorsLi, Xiaodong.
ContributorsNandi, Subhasis.
Source SetsUniversity of Victoria
Detected LanguageEnglish

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