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

Authors:
Sang-Hyuk Lee;Seong-Pyo Cheon;Yountae Kim;Sungshin Kim
Affiliations:
School of Electrical and Computer Engineering, Pusan National University, Busan, Korea;School of Electrical and Computer Engineering, Pusan National University, Busan, Korea;School of Electrical and Computer Engineering, Pusan National University, Busan, Korea;School of Electrical and Computer Engineering, Pusan National University, Busan, Korea
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

Citing 5
Cited 0

Ten lectures on wavelets

Ten lectures on wavelets
Entropy, distance measure and similarity measure of fuzzy sets and their relations

Fuzzy Sets and Systems
Fault diagnosis of machines via parameter estimation and knowledge processing: tutorial paper

Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Neural Networks for Identification, Prediction, and Control

Neural Networks for Identification, Prediction, and Control
Computational Statistics Handbook with MATLAB, Second Edition (Chapman & Hall/Crc Computer Science & Data Analysis)

Computational Statistics Handbook with MATLAB, Second Edition (Chapman & Hall/Crc Computer Science & Data Analysis)

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.