Extraction of induction motor fault characteristics in frequency domain and fuzzy entropy

Feature extraction for 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. The fuzzy membership function is also required to obtain the fuzzy entropy. The membership function is designed by the bootstrap method and central limit theorem. PCA (principal component analysis) and LDA (linear discriminant analysis) is finally applied to obtain characteristics of the healthy and faulted motors