Acoustic emission-based prognostics of slow rotating bearing using Bayesian techniques under dependent and independent samples

Show simple item record Aye, S.A. (Sylvester Aondolumun) Heyns, P.S. (Philippus Stephanus) 2016-03-04T06:02:05Z 2015-06
dc.description.abstract This study develops a novel degradation assessment index (DAI) from acoustic emission signals obtained from slow rotating bearings and integrates the same into alternative Bayesian methods for the prediction of remaining useful life (RUL). The DAI is obtained by the integration of polynomial kernel principal component analysis (PKPCA), Gaussian mixture model (GMM), and exponentially weighted moving average (EWMA). The DAI is then used as inputs in several Bayesian regression models, such as the multilayer perceptron (MLP), radial basis function (RBF), Bayesian linear regression (BLR), Gaussian mixture regression (GMR), and the Gaussian process regression (GPR) for RUL prediction. The combination of the DAI with the GPR model, otherwise known as the DAIGPR gave the best prediction with the least error. The findings show that the GPR model is suitable and effective in the prediction of RUL of slow rotating bearings and robust to varying operating conditions. Further, the findings are also robust when the training and tests sets are obtained from dependent and independent samples. Therefore, the GPR model is found useful for monitoring the condition of machines in order to implement effective preventive rather than reactive maintenance, thereby maximizing safety and asset availability. en_ZA
dc.description.embargo 2016-06-30
dc.description.librarian hb2015 en_ZA
dc.description.uri en_ZA
dc.identifier.citation S. A. Aye & P. S. Heyns (2015) Acoustic Emission-Based Prognostics of Slow Rotating Bearing Using Bayesian Techniques Under Dependent and Independent Samples, Applied Artificial Intelligence, 29:6, 563-596, DOI:10.1080/08839514.2015.103843. en_ZA
dc.identifier.issn 0883-9514 (print)
dc.identifier.issn 1087-6545 (online)
dc.identifier.other 10.1080/08839514.2015.1038432
dc.language.iso en en_ZA
dc.publisher Taylor and Francis en_ZA
dc.rights © 2015 Taylor & Francis Group, LLC. This is an electronic version of an article published in Applied Artificial Intelligence, vol. 29, no. 6, pp.563-596, 2015. doi : 10.1080/08839514.2015.1038432. Applied Artificial Intelligence is available online at : en_ZA
dc.subject Acoustic emission signals en_ZA
dc.subject Slow rotating bearings en_ZA
dc.subject Bayesian methods en_ZA
dc.subject Degradation assessment index (DAI) en_ZA
dc.subject Remaining useful life (RUL) en_ZA
dc.subject Polynomial kernel principal component analysis (PKPCA) en_ZA
dc.subject Gaussian mixture model (GMM) en_ZA
dc.subject Exponentially weighted moving average (EWMA) en_ZA
dc.title Acoustic emission-based prognostics of slow rotating bearing using Bayesian techniques under dependent and independent samples en_ZA
dc.type Postprint Article en_ZA

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