Radial basis function neural network for effective condition monitoring of rolling element bearing

Vijay, G S and Pai, Srinivasa P and Sriram, N S and Rao, Raj B K N (2015) Radial basis function neural network for effective condition monitoring of rolling element bearing. International journal of COMADEM, UK, 18 (3). pp. 21-31. ISSN 1363-7681

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The performance of the Radial Basis Function Neural Network (RBFNN) in the defect classification of a Rolling Element Bearing (REB) has been investigated in this work. The features (input) required for training the RBFNN have been extracted from the non-overlapping segments of the raw and denoised bearing vibration signals. A kurtosis based wavelet denoising method has been used to reduce the noise components in the vibration signals. The Fisher’s Criterion (FC) has been used to select a few sensitive features and form a reduced feature set. The centers of the RBF units have been optimized using a modified Fuzzy C-Means (FCM) algorithm, viz., Cluster Dependent Weighted FCM (CDWFCM). The performance of the RBFNN has been compared for four training strategies: two types of feature sets (all features and FC selected features) and two types of RBF centers (centers selected randomly and centers selected using CDWFCM). These strategies have also been tested for the bearing vibration signal provided by the Case Western Reserve University database.

Item Type: Article
Uncontrolled Keywords: Rolling element bearing, Neural network, Radial basis function, Wavelet denoising, Dimensionality reduction, Fuzzy C-Means, Fisher’s Criterion.
Subjects: Engineering > MIT Manipal > Mechanical and Manufacturing
Depositing User: MIT Library
Date Deposited: 26 Mar 2016 14:53
Last Modified: 26 Mar 2016 14:53
URI: http://eprints.manipal.edu/id/eprint/145655

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