Robust Voltage based Technique for Automatic Off-Line Detection of Static Eccentricity of PMSM

This work proposes an offline detection method for the presence of static eccentricity in Permanent Magnet Synchronous Machines, which is noninvasive and robust to small perturbations of parameters and the conditions. This detection method relies on classifier for fault detection. The classifier was trained using data, which incorporates variation due to rotor position misalignment, small load and speed changes. This makes the detection scheme robust under offline conditions with an assumption that temperature can be controlled in an acceptable range. The machine was controlled using Field Oriented Control (FOC) using Real Time LABVIEW. Two dimensional (2-D) Finite Element Analysis (FEA) was used to model and simulate the machine under healthy and faulty conditions to validate the results obtained from experiments.

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