Integration of Cost-risk Assessment of Denial of Service within an Intelligent Maintenance System

Abstract

As organisations become richer in data the function of asset management will have to increasingly use intelligent systems to control condition monitoring systems and organise maintenance. In the future the UK rail industry is anticipating having to optimize capacity by running trains closer to each other. In this situation maintenance becomes extremely problematic as within such a high-performance network a relatively minor fault will impact more trains and passengers; such denial of service causes reputational damage for the industry and causes fines to be levied against the infrastructure owner, Network Rail. Intelligent systems used to control condition monitoring systems will need to optimize for several factors; optimization for minimizing denial of service will be one such factor. With schedules anticipated to be increasingly complicated detailed estimation methods will be extremely difficult to implement. Cost prediction of maintenance activities tend to be expert driven and require extensive details, making automation of such an activity difficult. Therefore a stochastic process will be needed to approach the problem of predicting the denial of service arising from any required maintenance. Good uncertainty modelling will help to increase the confidence of estimates. This paper seeks to detail the challenges that the UK Railway industry face with regards to cost modelling of maintenance activities and outline an example of a suitable cost model for quantifying cost uncertainty. The proposed uncertainty quantification is based on historical cost data and interpretation of its statistical distributions. These estimates are then integrated in a cost model to obtain accurate uncertainty measurements of outputs through Monte-Carlo simulation methods. An additional criteria of the model was that it be suitable for integration into an existing prototype integrated intelligent maintenance system. It is anticipated that applying an integrated maintenance management system will apply significant downward pressure on maintenance budgets and reduce denial of service. Accurate cost estimation is therefore of great importance if anticipated cost efficiencies are to be achieved. While the rail industry has been the focus of this work, other industries have been considered and it is anticipated that the approach will be applicable to many other organisations across several asset management intensive industries.