Prediction of system reliability for single component repair
Purpose – To present a new Split System Model (SSM) that predicts the reliability of complex systems with multiple Preventive Maintenance (PM) actions in the long term. Design/methodology/approach - The SSM was developed using probability theory based on the concept of separating repaired and unrepaired components within a system virtually when modelling the reliability of the system after repairs. After theoretical analysis, a case study and Monte Carlo simulation were used to evaluate the effectiveness of the newly developed model. Findings – The model can be used to determine the remaining life of systems, to show the changes in reliability with PM actions, and to quantify PM intervals after imperfect repairs. Practical implications – SSM can be used to predict the reliability of complex systems with multiple PM actions, and hence can be used to support asset PM decision making over the whole life of the asset, such as scheduled PM times and spare parts requirements. An asset often has some vulnerable components, i.e., where the lives of these components are much shorter than the rest of the asset. In this case, PM is often conducted on these vulnerable components for maximising the useful life of the asset. The specific formulae derived in this paper can be used to predict the reliability of the asset for this scenario. Originality/value - The proposed model uses a new concept of split systems to predict the changes of reliability of complex systems with multiple PM actions. Asset managers will find this model to be a useful tool in the optimisation of their asset PM strategies.
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|Item Type:||Journal Article|
|Keywords:||Reliability prediction, Repairable system, Imperfect repair, Preventive maintenance|
|Divisions:||Current > QUT Faculties and Divisions > Division of Research and Commercialisation
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2007 Emerald|
|Deposited On:||12 Mar 2009 02:29|
|Last Modified:||29 Feb 2012 13:35|
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