Diagnostics and prognostics in rotating machinery is a subject of much on-going
research. There are three approaches to diagnostics and prognostics. These include
experience-based approaches, data-driven techniques and model-based techniques.
Bayesian data-driven techniques are gaining widespread application in diagnostics
and prognostics of mechanical and allied systems including slow rotating bearings, as
a result of their ability to handle the stochastic nature of the measured data well. The
aim of the study is to detect incipient damage of slow rotating bearings and develop
diagnostics which will be robust under changing operating conditions. Further it is
required to explore and develop an optimal prognostic model for the prediction of
remaining useful life (RUL) of slow rotating bearings.
This research develops a novel integrated nonlinear method for the effective feature
extraction from acoustic emission (AE) signals and the construction of a degradation
assessment index (DAI), which is subsequently used for the fault diagnostics of slow
rotating bearings. A slow rotating bearing test rig was developed to measure AE data
under variable operational conditions. The proposed novel DAI obtained by the
integration of the PKPCA (polynomial kernel principal component analysis), a
Gaussian mixture model (GMM) and an exponentially weighted moving average
(EWMA) is shown to be effective and suitable for monitoring the degradation of slow
rotating bearings and is robust under variable operating conditions. Furthermore, this
study integrates the novel DAI into alternative Bayesian methods for the prediction of
RUL. The DAI is used as input in several Bayesian regression models such as the multi-layer 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 DAI-GPR gives the best prediction. 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 models are also
robust when the training and tests sets are obtained from dependent and independent
samples.
Finally, an optimal GPR for the prediction of RUL of slow rotating bearings based on
a DAI is developed. The model performance is evaluated for cases where the training
and test samples from cross validation approach are dependent as well as when they
are independent. The optimal GPR is obtained from the integration or combination of
existing simple mean and covariance functions in order to capture the observed trend
of the bearing degradation as well as the irregularities in the data. The resulting
integrated GPR model provides an excellent fit to the data and improvements over the
simple GPR models that are based on simple mean and covariance functions. In
addition, it achieves a near zero percentage error prediction of the RUL of slow
rotating bearings when the training and test sets are from dependent samples but
slightly different values when the estimation is based on independent samples. These
findings are robust under varying operating conditions such as loading and speed. The
proposed methodology can be applied to nonlinear and non-stationary machine
response signals and is useful for preventive machine maintenance purposes.
Keywords: acoustic emission, Bayesian linear regression, Bayesian techniques,
covariance function, data-driven, degradation assessment index, diagnostics,
experience-based, exponentially weighted moving average, Gaussian mixture model,
Gaussian mixture regression, Gaussian process regression, integration, mean function,
model-based, multi-layer perceptron, polynomial kernel principal component
analysis, prognostics, radial basis function, remaining useful life. / Thesis (PhD)--University of Pretoria, 2014. / lk2014 / Mechanical and Aeronautical Engineering / PhD / unrestricted
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:up/oai:repository.up.ac.za:2263/43129 |
| Date | January 2014 |
| Creators | Aye, S.A. (Sylvester Aondolumun) |
| Contributors | Heyns, P.S. (Philippus Stephanus), sylvester.a.aye@gmail.com |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | © 2014 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
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