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Fault detection in Induction motor using WPT and Multiple SVM

Publication Type : Journal Article

Publisher : International Journal of Control and Automation

Source : International Journal of Control and Automation, Volume 3, Number 2, p.9-20 (2010)

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Keywords : Accelerometers, Bearing fault, Bearings (machine parts), Electrical faults, Hall-effect sensors, Induction motors, Mechanical faults, Motor current signature analysis, Multi-class classification, Multi-scale Decomposition, Neural networks, Signal processing, Support vector machines, Vibrations (mechanical), Wavelet packet transforms

Campus : Kochi

School : School of Engineering

Department : Electronics and Communication

Year : 2010

Abstract : In this paper a novel approach to detect various faults occurring in the induction motor is presented. Both vibration and motor current signature analysis are performed to detect the mechanical and electrical faults. Multi-scale decomposition process using wavelet packet transform is performed on the obtained signal to extract the features. The extracted features are given to a classifier to identify whether a fault has occurred. If a fault exists, it identifies the fault location and isolates it. The various faults discussed in this paper are: mechanical faults- such as bearing faults and electrical faults occurring in the rotor and stator parts of an induction motor. Multiple Support Vector Machine using the one-against-others approach is used to obtain multi-class classification of fault.

Cite this Research Publication : K. B. Aravindh, Saranya, G., Selvakumar, R., R. Shree, S., Saranya, M., and Sumesh, E. P., “Fault detection in Induction motor using WPT and Multiple SVM”, International Journal of Control and Automation, vol. 3, pp. 9-20, 2010.

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