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- W2183679022 abstract "Due to their advantages over other electrical motors, highly reliable three phase induction motor plays crucial role in the industrial applications. Most of the researchers have considered motor-diagnosis techniques to prevent sudden stops by the main sources of faults; 1.Bearingfaults 2.Statorfaults 3. Rotor Faults 4. Eccentricity Faults. The lack of an accurate coherent model is the main difficult to describe a faulty motor. The objective of this paper proposed a Self Organizing Map-Support Vector Machine (SOM-SVM) to accurate classification. From the faults of the induction motor and the sensor signal information, Wavelet Packet Transformation (WPT) is utilized to detect and extract the fault feature vectors from stationary or non-stationary signals. Then SOM-SVM classifies the pattern information. The results show that the detection of the fault, identification of the fault is improved and more accurately classified." @default.
- W2183679022 created "2016-06-24" @default.
- W2183679022 creator A5029584548 @default.
- W2183679022 date "2014-01-01" @default.
- W2183679022 modified "2023-09-26" @default.
- W2183679022 title "A Novel Faulty Diagnosis Technique of Three-Phase Induction Motor Based on Wavelet Packets and SOM-SVM" @default.
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- W2183679022 hasPublicationYear "2014" @default.
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