Development of a state prediction model to aid decision making in condition based maintenance

Hussin, B

Abstract

Condition monitoring and fault diagnosis for operational equipment are developing and
showing their potential for enhancing the effectiveness and efficiency of maintenance
management, including maintenance decision-making. In this thesis, our aim is to
model the condition of equipment items subject to condition-monitoring in order to
provide a quantitative measure to aid maintenance decision-making. A key ingredient
towards dealing with the modelling work is to define the state or condition of the
equipment with an appropriate measure and the observed condition monitoring may be
a function of the state or condition of the operational equipment concerned. This leads
to the two elements that are important in our modelling development; the need to
develop a model that describes the system condition subject to its monitoring data and a
decision model that is based upon the predicted system condition.
A quantification of the system condition in this thesis is modelled using either discrete
or continuous measures. In the case of a discrete state space, this thesis presents details
of how the initiation of a random defect can be identified. In the case of a continuous
state space, two approaches, which were used to identify the system condition, are
discussed. The first is adopted from the concept of the conditional residual time and
secondly, a wear process determined from a beta distribution. In developing these
models, we used vibration and oil analysis data. Note that understanding, manipulating
and analysing of the data played an important role in this thesis. This is needed not only
for model development, but also for validating the model. Methods for estimating
model parameters are discussed in detail. In addition, since the models presented are
generally beyond the scope for analytical solutions, two numerical approximation
methods are proposed. Simple decision models, which minimize the expected cost per
unit time over a time interval between the current monitoring time and the next
monitoring time, are shown. Numerical examples to demonstrate the modelling ideas
are also illustrated throughout the thesis.

Citation

Hussin, B. Development of a state prediction model to aid decision making in condition based maintenance. (Thesis). University of Salford

• Thesis Type: Thesis
• Deposit Date: Aug 18, 2021
• Additional Information: Funders : Universiti Teknikal Malaysia Melaka;Public Service Department of Malaysia
• Award Date: Mar 1, 2007