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Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data

Ali, Yasir Hassan and Abd Rahman, Roslan and Raja Hamzah, Raja Ishak (2015) Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data. Shock And Vibration, 2014 . ISSN 1070-9622

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Official URL: http://dx.doi.org/10.1155/2015/106945

Abstract

The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN) computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (). The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.

Item Type:Article
Uncontrolled Keywords:backpropagation, backpropagation algorithms, damage detection, elastohydrodynamic lubrication, film thickness, gears, lubricating oils, neural networks, oil shale, spur gears
Subjects:A General Works
ID Code:57894
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 12:07
Last Modified:21 Feb 2017 16:47

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