Picture of blood cells

Open Access research which pushes advances in bionanotechnology

Strathprints makes available scholarly Open Access content by researchers in the Strathclyde Institute of Pharmacy & Biomedical Sciences (SIPBS) , based within the Faculty of Science.

SIPBS is a major research centre in Scotland focusing on 'new medicines', 'better medicines' and 'better use of medicines'. This includes the exploration of nanoparticles and nanomedicines within the wider research agenda of bionanotechnology, in which the tools of nanotechnology are applied to solve biological problems. At SIPBS multidisciplinary approaches are also pursued to improve bioscience understanding of novel therapeutic targets with the aim of developing therapeutic interventions and the investigation, development and manufacture of drug substances and products.

Explore the Open Access research of SIPBS. Or explore all of Strathclyde's Open Access research...

The effect of model uncertainty on maintenance optimization

Bedford, T.J. and Bunea, C. (2002) The effect of model uncertainty on maintenance optimization. IEEE Transactions on Reliability, 51 (4). pp. 486-493. ISSN 0018-9529

Full text not available in this repository.Request a copy from the Strathclyde author


Much operational reliability data available, e.g., in the nuclear industry, is heavily right-censored by preventive maintenance. The common methods for dealing with right-censored data (total time on test statistic, Kaplan-Meier estimator, adjusted rank methods) assume the s-independent competing-risk model for the underlying failure process and the censoring process, even though there are, many s-dependent competing-risk models that can also interpret the data. It is not possible to identify the 'correct' competing risk model from censored data. A reasonable question is whether this model uncertainty is of practical importance. This paper considers the impact of this model-uncertainty on maintenance optimization, and shows that it can be substantial. Three competing-risk model classes are presented which can be used to model the data, and determine an optimal maintenance policy. Given these models, then consider the error that is made when optimizing costs using the wrong model. Model uncertainty can be expressed in terms of the 'dependence between competing risks' which can be quantified by expert judgment. This enables reformulating the maintenance optimization problem to account for model uncertainty.