Maintenance is an essential task that must be carried out in an efficient and effective manner in order to sustain and prolong the physical assets of a company. Maintenance may be defined as any action which has the objective of retaining or restoring an item to a state in which it can perform its required function. Maintenance is therefore a valuable part of most industries today, helping improve productivity and output whilst reducing the costs associated with downtime in addition to eliminating failure of equipment. The goal of maintenance, like all other functions of any manufacturing company, must be a cost effective activity. Consequently, it becomes essential for a company to develop a cost effective maintenance strategy that will achieve this goal. Delay-time analysis is a maintenance modelling technique which can achieve such goals in a manufacturing environment. Delay-time analysis, through the input of certain parameters, is capable of establishing an optimum inspection interval from both a downtime standpoint as well as a cost standpoint. The delay-time analysis concept has been further developed in this thesis in order to establish an environmental model. Alongside the downtime model and cost model, the environmental model gives a measure of the consequence of failure in terms of cost to both a company and to the environment. This environmental model has been applied to a company producing a product which is potentially harmful to both humans and the environment. The use of delay-time analysis to establish a downtime model and cost model relies predominantly on objective historical data which, given the correct types of data, makes model development a powerful and accurate tool. The environmental model, however, relies heavily on subjective data and expert judgement in order to establish the required parameters. In order to overcome the inevitable inaccuracies present in subjective expert judgement, due mainly to individual perception, the environmental model has been further enhanced using fuzzy set modelling. The use of delay-time analysis to develop a model involves establishing several important parameters, one such parameter being that of failure rate (λ). This parameter forms an integral part of a delay-time analysis study but is established in a simplistic manner (i. e. number of failures/time). This parameter is established using historical information calculated using statistical averages. Understanding and identifying the influencing factors responsible for failure will serve to improve the understanding and increase accuracy of failure rate. This thesis examines and develops this parameter with the use of Bayesian network modelling. Bayesian network modelling allows differing influences responsible for failure to be considered in an exact and precise manner. The findings of this research is that a methodology has been successfully developed, using delay-time analysis modelling, in order to aid decision making in a manufacturing environment. Further improvement of the delay-time analysis model was brought about with the use of fuzzy set modelling and Bayesian network modelling. The integration of both the fuzzy set model and Bayesian network model into the delay-time model has been conducted. A direct comparison has being drawn between the original delay-time model and the enhanced delay-time model in order to highlight the improvements of the integrated model.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:502749 |
| Date | January 2009 |
| Creators | Jones, Bryan James |
| Publisher | Liverpool John Moores University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://researchonline.ljmu.ac.uk/5904/ |
Page generated in 0.002 seconds