Fault classification performance of induction motor bearing using AI methods

Mahamad, Abd Kadir and Hiyama, Takashi (2010) Fault classification performance of induction motor bearing using AI methods. In: 2010 the 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), 15-17 June 2010, Taichung.

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Abstract

This paper presents an approach of intelligent fault classification of induction motor bearing (IMB) using several artificial intelligent (AI) methods. The most common AI methods are FeedForward Neural Network (FFNN), Elman Network (EN),Radial Basis Function Network (RBFN) and Adaptive NeuroFuzzy Inference System (ANFIS). The data of IMB fault is obtained from 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 isselected.

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > TA Engineering (General). Civil engineering (General) > TA174 Engineering design
Divisions:Faculty of Electrical and Electronic Engineering > Department of Computer Engineering
ID Code:3028
Deposited By:Normajihan Abd. Rahman
Deposited On:14 Feb 2013 14:36
Last Modified:21 Jan 2015 15:56

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