Fourier and wavelet transformations application to fault detection of induction motor with stator current |
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Authors: | Sang-hyuk Lee Yi-qi Wang and Jung-il Song |
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Affiliation: | School of Mechatronics, Changwon National University, Changwon 641-773, Korea |
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Abstract: | Fault detection of an induction motor was carried out using the information of the stator current. After synchronizing the
actual data, Fourier and wavelet transformations were adopted in order to obtain the sideband or detail value characteristics
under healthy and various faulty operating conditions. The most reliable phase current among the three phase currents was
selected using an approach that employs the fuzzy entropy measure. Data were trained with a neural network system, and the
fault detection algorithm was verified using the unknown data. Results of the proposed approach based on Fourier and wavelet
transformations indicate that the faults can be properly classified into six categories. The training error is 5.3×10?7, and the average test error is 0.103. |
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Keywords: | Fourier transformation wavelet transformation induction motor fault detection |
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