Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490430
Title: Development of a state prediction model to aid decision making in condition based maintenance
Author: Hussin, Burairah
ISNI:       0000 0001 3585 2770
Awarding Body: University of Salford
Current Institution: University of Salford
Date of Award: 2007
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Thesis embargoed until 31 Jul 2022
Access from Institution:
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.
Supervisor: Not available Sponsor: Universiti Teknikal Malaysia Melaka ; Public Service Department of Malaysia
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.490430  DOI: Not available
Share: