Fourier and wavelet transformations for the fault detection of induction motor with stator current

In this literature, fault detection of an induction motor is carried out using the information of stator current. After preprocessing actual data, Fourier and wavelet transforms are applied to detect characteristics under the healthy and various faulted conditions. The most reliable phase current among 3-phase currents is selected by the fuzzy entropy. Data are trained with a neural network system, and the fault detection algorithm is carried out under the unknown data. The results of the proposed approach based on Fourier and wavelet transformations show that the faults are properly classified into six categories.

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