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Fault classification based artificial intelligent methods of induction motor bearing

Mahamad, Abd Kadir and Takashi, Hiyama (2010) Fault classification based artificial intelligent methods of induction motor bearing. International Journal of Innovative Computing, Information and Control, 6 (4). pp. 1-16. ISSN 13494198

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This paper presents an approach of intelligent fault classification of induction motor bearing (IMB) using several artificial intelligent (AI) methods. The significant of this work is to select appropriate method among the common AI methods. The most common AI methods includes FeedForward Neural Network (FFNN), Elman Network (EN), Radial Basis Function Network (RBFN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). In this work, the data of IMB fault was obtained from a Case Western Reserve University website in form of vibration signal. For further analysis, these datas are converted from time domain into frequency domain through Fast Fourier Transform (FFT) in order to acquire more fault signs during pre-processing stage. Then, during features extraction stage, a set of 16 features from vibration and pre-processing signal are extracted. Subsequently, a distance evaluation technique is used as features selection, in order to select only salient features. Lastly, during fault classification, several AI methods are examined, where results are compared and the optimum AI method is selected. Keywords: Induction motor bearing, FeedForward Neural Network, Elman Network, Radial Basis Function Network, Adaptive Neuro-Fuzzy Inference System.

Item Type: Article
Uncontrolled Keywords: Induction motor bearing; FeedForward neural network; Elman network; radial basis function network; adaptive neuro-fuzzy inference system
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/9427
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