Induction Motor Fault Diagnosis Based on Fuzzy Support Vector Machine

Shuying Li

2018

Abstract

In order to solve the problem of correctly identifying fault classes in induction motor fault diagnosis and improve the accuracy of the classification, a novel fault diagnosis method of the classification model based on the Fuzzy Support Vector Machine (FSVM) is proposed in this paper. The fault pattern classifier was trained, which the fuzzy membership of the feature vectors is computed by a controllable factors algorithm membership function to overcome the sensitivity to noise and outliers. After the stator current was sampled, the fault feature was extracted from the sampling data through wavelet analysis and used as the input of the FSVM. A multi-class fault classifier was constructed to identify different faults, which was based on one to one strategy and mixed matrix combination. Experiment results show that the Fuzzy Support Vector Machine (FSVM) has good performance for classification over non-linear and high dimension and small sample sets. This method improves the accuracy in rotor fault diagnosis.

Paper Citation

in Harvard Style

Li S. (2018). Induction Motor Fault Diagnosis Based on Fuzzy Support Vector Machine.In 3rd International Conference on Electromechanical Control Technology and Transportation - Volume 1: ICECTT, ISBN 978-989-758-312-4, pages 588-592. DOI: 10.5220/0006975105880592

in Bibtex Style

@conference{icectt18,
author={Shuying Li},
title={Induction Motor Fault Diagnosis Based on Fuzzy Support Vector Machine},
booktitle={3rd International Conference on Electromechanical Control Technology and Transportation - Volume 1: ICECTT,},
year={2018},
pages={588-592},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006975105880592},
isbn={978-989-758-312-4},
}

in EndNote Style

TY - CONF

JO - 3rd International Conference on Electromechanical Control Technology and Transportation - Volume 1: ICECTT,
TI - Induction Motor Fault Diagnosis Based on Fuzzy Support Vector Machine
SN - 978-989-758-312-4
AU - Li S.
PY - 2018
SP - 588
EP - 592
DO - 10.5220/0006975105880592