Bearing Diagnostics – A Radial Basis Function Neural Network Approach

Vijay, G S and Pai, Srinivasa P and Sriram, N S and Rao, Raj B K N (2011) Bearing Diagnostics – A Radial Basis Function Neural Network Approach. In: International Conference COMADEM 2011 (24th International Congress on Condition Monitoring and Diagnostics Engineering Management, Advances in Industrial Asset Integrity Management, 30th May – 1st June, , Clarion Hotel Stavanger Stavanger, Norway.

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Abstract

Rolling Element Bearings (REBs) play an important role in the condition monitoring of machines. The REBs are the main causes of breakdown of rotating machines. Vibration signal analysis has been extensively used for bearing fault diagnostics. In the efforts towards Intelligent Condition Monitoring and fault diagnostics, Artificial Neural Networks (ANNs) has been widely used. Multi-layer perceptrons (MLPs) are the most commonly used neural network (NN) architectures. Radial Basis Function (RBF) neural network architecture is not widely used for REB diagnostics. They are a relatively new class of NNs which have the advantages of simplicity, ease of implementation, excellent learning and generalization abilities. In this paper, RBF NN architecture has been used for fault diagnostics of REBs using vibration signal features. Using a customized bearing test rig, experiments have been carried out on a deep groove ball bearing namely 6205 under two different speeds and one load condition. The diagnostics is mainly concerned with classifying the bearing into two classes namely ‘Normal’ and ‘Used’. The performances of different learning strategies namely fixed centers (FC) selected at random, self-organized selection of centers using clustering algorithms – Fuzzy C Means (FCM), Density Weighted Fuzzy C Means (DWFCM) & Cluster Dependent Weighted Fuzzy C Means (CDWFCM) in designing the RBF neural network – have been compared. It has been found that basic FCM and CDWFCM give higher performance accuracy when compared to other strategies

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Rolling Element Bearing, Vibration signal analysis, Artificial Neural Networks, Radial Basis Function, Fuzzy C-Means
Subjects: Engineering > MIT Manipal > Mechanical and Manufacturing
Depositing User: MIT Library
Date Deposited: 25 Mar 2013 04:48
Last Modified: 25 Mar 2013 04:48
URI: http://eprints.manipal.edu/id/eprint/79254

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