Fault Diagnosis of Rolling Element Bearings using Vibration Signal Analysis – A Soft Computing Approach

Pai, Srinivasa P and Vijay, G S and Sriram, N S (2011) Fault Diagnosis of Rolling Element Bearings using Vibration Signal Analysis – A Soft Computing Approach. NMAMIT Annual Research , 1. pp. 44-51. ISSN 2249-0426,

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Vibration signal analysis is a widely used technique for monitoring the condition of rolling element bearings (REB). Artificial Neural Networks (ANN) and Support Vector Machines (SVM) are widely used soft computing techniques in fault diagnosis of REBs, along with Genetic Algorithms (GA). In this paper, ANNs and SVM have been used for fault diagnosis of REBs. Vibration signals collected using accelerometers mounted on the bearing housing, have been used to analyse the bearing condition. Normal bearing and defective bearings (with inner and outer race defect) have been analysed and the vibration signal features namely RMS, 0-P, crest factor, load and speed were used as inputs to train and test two types of ANNs namely Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural network. The results obtained have been compared with that obtained using SVM. It was found that MLP trained using different training algorithms gave an accuracy of around 85 %, whereas for RBF networks, it was around 60 % and for SVM it was around 73 % on test data. Thus MLP and SVMs were effective, when compared to RBF networks in fault diagnosis of REBs.

Item Type: Article
Uncontrolled Keywords: Rolling Element Bearing, Vibration Signals, Multi-Layer Perceptron, Neural Networks, Radial Basis Function, Support Vector Machine
Subjects: Engineering > MIT Manipal > Mechanical and Manufacturing
Depositing User: MIT Library
Date Deposited: 23 Mar 2013 11:36
Last Modified: 23 Mar 2013 11:36
URI: http://eprints.manipal.edu/id/eprint/79255

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