Feature form extraction and optimization of induction machine faults using PSO technique

Author

Medoued, Ammar ; Lebaroud, Abdesselam ; Laifa, Abdelaziz ; Sayad, D.

Author_Institution

Dept. de Genie Electr., Univ. du 20 Aout, Skikda, Algeria

fYear

2013

fDate

2-4 Oct. 2013

Firstpage

1

Lastpage

5

Abstract

This paper presents a diagnosis method for induction machine faults investigation. The method is based on feature extraction and optimization. The feature form extraction is based on the time-frequency representation (TFR), which is designed for maximizing the separability between classes. A distinct TFR is designed for each fault class. The PSO algorithm is used for the feature form optimization. The classifier is designed with an artificial neural network. This method is validated on a 5.5-kW induction motor test bench.

Keywords

electric machine analysis computing; fault diagnosis; induction motors; neural nets; particle swarm optimisation; time-frequency analysis; ANN; PSO technique; TFR; artificial neural network; fault class; fault diagnosis method; feature form extraction; feature form optimization; induction machine faults; induction motor test bench; particle swarm optimisation; power 5.5 kW; time-frequency representation; Discrete wavelet transforms; Inverters; Kernel; Monitoring; Neurons; Oils; Robustness; ANN; Induction Machine Diagnosis; PSO; Time-Frequency;

fLanguage

English

Publisher

ieee

Conference_Titel

Electric Power and Energy Conversion Systems (EPECS), 2013 3rd International Conference on

Conference_Location

Istanbul

Print_ISBN

978-1-4799-0687-1

Type

conf

DOI

10.1109/EPECS.2013.6713029

Filename

6713029