Fault Detection of Induction Motors Using Fourier and Wavelet Analysis
Hyeon Bae*, Youn-Tae Kim**, Sungshin Kim**, Sang-Hyuk Lee**, and Bo-Hyeun Wang***
*Research Institute of Computer Information and Communication, Pusan National University, 30 Jangjeon-dong, Geumjeong-gu, Busan 609-735, Korea
**School of Electrical and Computer Engineering, Pusan National University, 30 Jangjeon-dong, Geumjeong-gu, Busan 609-735, Korea
***Faculty of Electrical Engineering and Information Technology, Kangnung National University, 120 Gangneung Daehangno, Gangneung City, Gangwon Province 210-702, Korea
The motor is the workhorse of industries. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces fault detection for induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is applied to display signals. The Fourier Transform is employed to convert signals. After signal conversion, signal features must be extracted by signal processing such as wavelet and spectrum analysis. Features are entered in a pattern classification model such as a neural network model, a polynomial neural network, or a fuzzy inference model. This paper describes fault detection results that use Fourier and wavelet analysis. This combined approach is very useful and powerful for detecting signal features.
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