On-line rotor fault detection in squirrel cage induction motors based on wavelet analysis

2010 ◽  
Author(s):  
Wang Li ◽  
Wang Xuan ◽  
Wang Dongxu
Keyword(s):  
Fault Detection ◽  
Wavelet Analysis ◽  
Induction Motors ◽  
Squirrel Cage ◽  
On Line

Fault Detection of Induction Motors Using Fourier and Wavelet Analysis

2004 ◽  
Vol 8 (4) ◽  
pp. 431-436 ◽  
Author(s):  
Hyeon Bae ◽  
◽  
Youn-Tae Kim ◽  
Sungshin Kim ◽  
Sang-Hyuk Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Wavelet Analysis ◽  
Time Domain ◽  
Induction Motors ◽  
Fuzzy Inference ◽  
Classification Model ◽  
Diagnosis And Prognosis ◽  
Signal Features ◽  
The Time Domain

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.

Dynamical models for fault detection in squirrel cage induction motors

2007 ◽  
Vol 3 (1/2) ◽  
pp. 161 ◽  
Author(s):  
H. Rodriguez Cortes ◽  
C.N. Hadjicostis ◽  
A.M. Stankovic
Keyword(s):  
Fault Detection ◽  
Induction Motors ◽  
Dynamical Models ◽  
Squirrel Cage
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