DOI: https://doi.org/10.14710/ijred.7.1.43-52

Automatic and Online Detection of Rotor Fault State

1Department of Electrical Engineering, University 20 Aout 1955-Skikda. B.P.26 Route El-Hadaiek, Skikda 21000, Algeria

2Department of Mechanical Engineering, University 20 Aout 1955-Skikda. B.P.26 Route El-Hadaiek, Skikda 21000, Algeria

Published: 18 Feb 2018.
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Abstract

In this work, we propose a new and simple method to insure an online and automatic detection of faults that affect induction motor rotors. Induction motors now occupy an important place in the industrial environment and cover an extremely wide range of applications. They require a system installation that monitors the motor state to suit the operating conditions for a given application. The proposed method is based on the consideration of the spectrum of the single-phase stator current envelope as input of the detection algorithm. The characteristics related to the broken bar fault in the frequency domain extracted from the Hilbert Transform is used to estimate the fault severity for different load levels through classification tools. The frequency analysis of the envelope gives the frequency component and the associated amplitude which define the existence of the fault. The clustering of the indicator is chosen in a two-dimensional space by the fuzzy c mean clustering to find the center of each class. The distance criterion, the K-Nearest Neighbor (KNN) algorithm and the neural networks are used to determine the fault type. This method is validated on a 5.5-kW induction motor test bench.

Article History: Received July 16th 2017; Received: October 5th 2017; Accepted: Januari 6th 2018; Available online

How to Cite This Article: Ouanas, A., Medoued, A., Haddad, S., Mordjaoui, M., and Sayad, D. (2017) Automatic and online Detection of Rotor Fault State. International Journal of Renewable Energy Development, 7(1), 43-52.

http://dx.doi.org/10.14710/ijred.7.1.43-52
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Keywords: broken bar fault; Fuzzy c mean clustering; Hilbert Transform; neural network; KNN; envelope

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Section: Original Research Article
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