| Title |
Development of Induction Motor Diagnosis Method by Variance Based Feature Selection and PCA-ELM |
| Authors |
Dae-Jong Lee ; Myung-Geun Chun |
| DOI |
http://dx.doi.org/10.5207/JIEIE.2010.24.8.055 |
| Keywords |
Feature Selection ; ELM ; PCA ; Induction Motor ; Fault Detection |
| Abstract |
In this paper, we proposed selective extraction method of frequency information and PCA-ELM based diagnosis system for three-phase induction motors. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by variance As the next step, feature extraction is performed by principal component analysis (PCA). Finally, we used the classifier based on Extreme Learning Machine (ELM) with fast learning procedure. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load. |