A comparison of models for measurable deterioration: an application to coating on steel structures

Steel structures like bridges, tanks and pylons are exposed to outdoor weathering conditions. In order to prevent them from corrosion they are protected by organic coating systems. This paper focuses on modelling the deterioration of the organic coating layer that protects steel structures from corrosion. Only if there is sufficient knowledge of the condition of the coating on these structures, maintenance actions can be done in the most efficient way. Therefore the course of the deterioration of the coating system and its lifetime, which is also of importance for doing maintenance, have to be assessed accurately. In this paper three different stochastic processes, viz. Brownian motion with non-linear drift, the non-stationary gamma process and a two-stage hit-and-grow physical process, are fitted to two real data sets. In this way we are the first who compare the three stochastic processes empirically on criteria such as goodness-of-fit, computational convenience and ease of implementation. The first data set is based on expert judgement; the second consists of inspection results. In the first case the model parameters are obtained by a least squares approach, in the second case by the method of maximum likelihood. A meta-analysis is performed on the two-stage hit-and-grow model by means of fitting Brownian motion and gamma process to the outcomes of this model

Expert judgement in maintenance optimization

this paper proposes a compehensive method for the use of expert opinion for obtaining lifetime distributions required for maintenance optimization. The method includes procedures for the elicitation of discretized lifetime distributions from several experts, the combination of the elicited expert opinion into a consensus distribution, and the updating of the consensus distribution with failure and maintenance data. The method was motivated by the practical circumstances governing its implementation. In particular, by the lack of statistical training of the experts an the high demands on their time. The use of a discretized life distibution provides more flexibility, is more comprehendible by the experts in the elicitation stage, and greatly reduces the computation in the combination and updating stages. The methodology is Bayes, using the Dirichlet distribution as the prior distribution for the elicited discrete lifetime distribution. Methods are described for incorporating information concerning the expertise of the experts into the analysis

The Elicitation and use of expert judgment for Maintenance Optimization

Herein, we present an overviw of the elicitation and use of expert opinion in developing optimal maintenance policies. The procedure developed is based on restrictions found in practice. That is, where the "expert" has little statistical training and the elicitation process must be performed in a clear and quick manner. Due to these restrictions, a histogram is elicited form the expert and feedback and analysis is based on combining the elicited histogram with a fitted right tail to form a continuous distribution. Expressions for the pdf, failure rate, percentile life, and mean life are developed and used to calculate the optimal maintenance interval for given cost dat