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Evaluates the performance of a three-phase induction motor under mixed eccentric condition. Formulation of online tools for machine-condition monitoring; Inclusion of the finite-element results to substantiate the inductance values; Validation of the analysis by the line-current spectrum of the eccentric machine.

Early detection and diagnosis of incipient faults is desirable for online condition assessment, product quality assurance, and improved operational efficiency of induction motors. In this paper, a speed-sensorless fault diagnosis system is developed for induction motors, using recurrent dynamic neural networks and multiresolution or Fourier-based signal processing for transient or quasi-steady-state operation, respectively. In addition to nameplate information required for the initial system setup, the proposed fault diagnosis system uses only motor terminal voltages and currents. The effectiveness of the proposed diagnosis system in detecting the most widely encountered motor electrical and mechanical faults is demonstrated through extensive staged faults. The developed system is scalable to different power ratings and it has been successfully demonstrated with data from 2.2-, 373-, and 597-kW induction motors. [ABSTRACT FROM AUTHOR]

An online technique for monitoring the condition of the groundwall (GW) and phase-to-phase (PP) insulation for three-phase ac-machine stator windings is proposed in this paper. Insulation-condition indicators such as capacitance, dissipation factor (tan δ), and ac insulation resistance are calculated online based on the differential leakage-current measurements for each phase. A model for a three-phase ac-machine insulation system is derived and analyzed for both offline and online testing. Guidelines for interpreting the measured indicators for determining the: 1) overall condition and 2) significant degradation in the individual GW and PP insulation for each phase of the stator are given based on the analysis. Experimental results on a 15-hp induction motor under intentional GW and PP insulation degradation conditions show that incipient insulation degradation can be detected and classified with high sensitivity. [ABSTRACT FROM AUTHOR]

Investigates the selection criteria of induction machines for speed-sensorless drive applications. Details of the fundamental pole pair and rotor slot relationships for the generation of principal-slot-harmonic; Simulation results; Conclusions.