Condition monitoring is very important for critical assets such as gearboxes used in
the power and mining industries. Fluctuating operating conditions are inevitable for
wind turbines and mining machines such as bucket wheel excavators and draglines
due to the continuous
uctuating wind speeds and variations in ground properties,
respectively. Many of the classical condition monitoring techniques have proven to
be ine ective under
uctuating operating conditions and therefore more sophisticated
techniques have to be developed. However, many of the signal processing tools that
are appropriate for
uctuating operating conditions can be di cult to interpret, with
the presence of incipient damage easily being overlooked.
In this study, a cost-e ective diagnostic methodology is developed, using machine learning
techniques, to diagnose the condition of the machine in the presence of
operating conditions when only an acceleration signal, generated from a gearbox during
normal operation, is available. The measured vibration signal is order tracked to
preserve the angle-cyclostationary properties of the data. A robust tacholess order
tracking methodology is proposed in this study using probabilistic approaches. The
measured vibration signal is order tracked with the tacholess order tracking method
(as opposed to computed order tracking), since this reduces the implementation and
the running cost of the diagnostic methodology.
Machine condition features, which are sensitive to changes in machine condition, are extracted
from the order tracked vibration signal and processed. The machine condition
features can be sensitive to operating condition changes as well. This makes it difficult to ascertain whether the changes in the machine condition features are due to changes
in machine condition (i.e. a developing fault) or changes in operating conditions. This
necessitates incorporating operating condition information into the diagnostic methodology
to ensure that the inferred condition of the machine is not adversely a ected
uctuating operating conditions. The operating conditions are not measured
and therefore representative features are extracted and modelled with a hidden Markov
model. The operating condition machine learning model aims to infer the operating
condition state that was present during data acquisition from the operating condition
features at each angle increment. The operating condition state information is used
to optimise robust machine condition machine learning models, in the form of hidden
The information from the operating condition and machine condition models are combined
using a probabilistic approach to generate a discrepancy signal. This discrepancy
signal represents the deviation of the current features from the expected behaviour of
the features of a gearbox in a healthy condition. A second synchronous averaging
process, an automatic alarm threshold for fault detection, a gear-pinion discrepancy
distribution and a healthy-damaged decomposition of the discrepancy signal are proposed
to provide an intuitive and robust representation of the condition of the gearbox
uctuating operating conditions. This allows fault detection, localisation as well
as trending to be performed on a gearbox during
uctuating operation conditions.
The proposed tacholess order tracking method is validated on seven datasets and the
fault diagnostic methodology is validated on experimental as well as numerical data.
Very promising results are obtained by the proposed tacholess order tracking method
and by the diagnostic methodology.
Dissertation (MEng)--University of Pretoria, 2017.