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 (λ). 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.
Highlights
Predicting the performance of a gear system is a serious function, as it is a crucial component in machinery
The result indicates that the performance of networks in training is better than in testing for both Elman and Feed-Forward Back-Propagation Neural Networks (FFBP), whereas the value of the errors measurement in training is less than in testing. (Note that the difference in error between the networks is much larger during training as compared to testing.)
An artificial neural network (ANN) techniques approach was proposed to improve the accuracy of oil film thickness prediction for spur gear
Summary
Predicting the performance of a gear system is a serious function, as it is a crucial component in machinery. Periodic inspection is essential in gear teeth or bearings, so that crack propagation or the other damage can be identified beforehand. Typical failures in gears are generally associated to bending, fatigue, contact fatigue, wear, and scuffing, all of which can be monitored by testing vibration and acoustic signals, temperature, torque, and lubrication film thickness. This can be carried out through continuous or online monitoring. Damage to gear teeth alters the parameters originating in the gear shaft. Damage affects the oil film thickness and the type of wear that occurs [1]
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