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Induction motor bearing failure diagnosis with ANN and hybrid networks model

Mahamad, Abd Kadir and Takashi, Hiyama (2009) Induction motor bearing failure diagnosis with ANN and hybrid networks model. ICIC Express Letters, 3 (3(B)). pp. 543-548. ISSN 1881803X

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Abstract

This paper proposes five artificial intelligent (AI) methods to determine in- duction motor bearing (IMB) fault diagnosis. In this case, two artificial neural networks (ANN) which are Feedforward Neural Network (FFNN) and Elman Network (EN) and three hybrid networks, which are FFNN with GA (FFGA), EN with GA (ENGA), and adaptive network-based fuzzy inference system (ANFIS) are examined to classify IMB failure. The vibration signal used in this work is obtained from Case Western Reserve University website. During preprocessing stage, the vibration signal have been converted from time domain into frequency domain through Fast Fourier Transform (FFT). Then, an enveloping method is used to eliminate the high frequency components from the vi- bration signal. Subsequently, a set of 16 features from vibration and preprocessed signal is extracted. In order to reduce the size of data, a distance evaluation technique is used as features selection. Lastly, during fault diagnosis all AI methods are examined whose results are compared and conclusions are drawn.

Item Type: Article
Uncontrolled Keywords: Fault diagnosis; IMB; AI; ANN; distance evaluation technique
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Electrical and Electronic Engineering > Department of Computer Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 Aug 2018 03:37
Last Modified: 13 Aug 2018 03:37
URI: http://eprints.uthm.edu.my/id/eprint/9431
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