Artificial Neural Network based Condition Monitoring of Rolling Element Bearing

Vijay, G S and Pai, Srinivasa P and Sriram, N S (2010) Artificial Neural Network based Condition Monitoring of Rolling Element Bearing. In: Artificial Neural Network based Condition, 7th May, 2010.

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Abstract

Effectiveness of Artificial Neural Network (ANN) classifier for fault diagnosis of rolling element bearings (REB) is presented in this paper. Raw vibration signals (acceleration) acquired from normal and defective (Inner Race (IR) defect and Outer Race (OR) defect) bearings are used for feature extraction. Simple statistical features such as standard deviation, skewness, kurtosis etc. of the time-domain vibration signal segments are used as characteristic features which are given as input to the ANN classifier. A comparison between the performance of ANN for different number of neurons in the hidden layer and training algorithms is made. Results show that ANN can be used effectively in condition monitoring of REB.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Rolling Element Bearing, Condition Monitoring, Neural Network
Subjects: Engineering > MIT Manipal > Mechanical and Manufacturing
Depositing User: MIT Library
Date Deposited: 25 Mar 2013 04:54
Last Modified: 25 Mar 2013 04:54
URI: http://eprints.manipal.edu/id/eprint/79249

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