# Wavelet-LDA-neural network based short circuit occurrence detection in induction motor winding
> Asfani D.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_81255197064
Jurnal / Konferensi: Sdemped 2011 8th IEEE Symposium on Diagnostics for Electrical Machines Power Electronics and Drives
Tahun terbit: 2011
DOI: https://doi.org/10.1109/DEMPED.2011.6063644
Citations: 4

## Authors
- Asfani D.

## Abstract
The paper proposes the short circuit identification method for induction motor winding. Four states of motor operation are defined as normal operation, starting of short circuit, steady state short circuit and ending of short circuit. The neural network based detection system is utilized to distinguish these defined operation states. Motor current is processed using discrete wavelet transformation to extract energy component of high frequency signal, which is latterly used for variable detection. Three different wavelet types varied by five levels of transformation are evaluated using linear discriminant analysis (LDA) in order to obtain the most appropriate wavelet filter for detection task. A laboratory experiment is performed to validate the accuracy of the proposed method.

## Keywords
- Wavelet
- Linear discriminant analysis
- Induction motor
- Computer science
- Pattern recognition (psychology)
- Transformation (genetics)
- Wavelet transform
- Artificial intelligence
- SIGNAL (programming language)
- Filter (signal processing)
- Artificial neural network
- Discrete wavelet transform
- Wavelet packet decomposition
- Control theory (sociology)
- Engineering
- Voltage
- Computer vision
- Chemistry
- Electrical engineering
- Control (management)
- Gene
- Biochemistry
- Programming language

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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