Your Maintenance Plan Can Only be as Good as the Information it is Based on

Two key elements in achieving maximum value from rail infrastructure investment are maintaining assets before they fail and avoiding servicing assets that don’t need it. Achieving the right balance depends on having a detailed and complete picture of asset condition.

Without this picture of the asset condition, operators have to rely on scheduled maintenance programmes – which are often conservative. Alternatively, they are dependent on a ‘find and fix’ approach or reacting when there is a failure. Neither option is likely to represent the most cost effective or risk-free approach.

Scheduled maintenance programmes may direct valuable resources to assets that are performing satisfactorily and that have a very low risk of failure. Meanwhile, other assets can be deteriorating more than expected. They may, unknown to the operator or maintenance team, need urgent attention.

Technology

Given the scale of the rail infrastructure, routine manual inspections are not a feasible way to build the detailed picture needed. This isn’t an option for delivering greater investment efficiency and value. Increasingly, technology is playing a part in building the database of asset condition knowledge needed.

Live, real time monitoring helps to focus investment on where it is really needed and where it can deliver the most value. Performance, dimensions or operating conditions of assets can be constantly monitored to provide an early warning of a likely failure, allowing planned remedial actions that minimise disruption and safety risks.

Detailed knowledge about assets can also be used to create a comprehensive risk-prioritised task list for each closure or possession. Without it, there will always be opportunities missed to carry out additional work or to deploy a wider range of specialist contractors that could delay or prevent future closures of a section of line or an asset being taken out of service.

Managing Risk

When you understand the asset condition and have historical data to analyse, you are in a position to evaluate the risk of a future failure occurring. The technology and algorithms to carry out this analysis and machine learning are evolving and will certainly play an increasing role in the future management of our rail infrastructure.

However, we get there, comprehensive, real time monitoring of asset condition is essential if we are to make the best possible use of infrastructure investment.

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