Publication Type : Journal Article
Publisher : Expert Systems with Applications
Source : Expert Systems with Applications, Volume 37, Number 1, p.450-455 (2010)
Keywords : Current signature analysis, Current signatures, Electric currents, Fault diagnostics, Feature extraction, Feature selection, Feature selection and classification, High frequency HF, Image retrieval, Induction motors, Industrial applications, Low frequency, Mathematical transformations, Motors, Pattern classification, Pattern classification problems, Pattern classification techniques, Stator fault, Stator winding faults, Stators, Support vector machine (SVM), Support vector machines, Time interval, Vector quantization, Wavelet transforms
Year : 2010
Abstract : 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. © 2009 Elsevier Ltd. All rights reserved.
Cite this Research Publication : Sa Radhika, Sabareesh, G. Ra, Jagadanand, Gb, and Sugumaran, Vc, “Precise wavelet for current signature in 3φsymbol IM”, Expert Systems with Applications, vol. 37, pp. 450-455, 2010.