Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
SVM practical industrial application for mechanical faults diagnostic
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Induction motors, which are used worldwide as the ''workhorse'' in industrial applications, are intermittently subjected to faults, mainly the stator faults. In this paper, fault diagnostics of induction motor using current signature analysis, with wavelet transform, is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, feature selection and classification. The feature extraction is done by wavelet transforms, using different wavelets which allow the use of long time intervals where there is precise low-frequency information, and shorter regions where there is precise high-frequency information. The extracted features are classified using the new generation pattern classification technique of Support Vector Machine (SVM) identification. Then the relative capability of the different wavelets, in performing the stator winding fault identification is analyzed and the best wavelet is selected.