Predictive maintenance: What is the opportunity in rail?

Predictive maintenance: What is the opportunity in rail?


Future-gazers in rail imagine a digital world in which the behaviour of every train component and subassembly is captured and recorded. Simon Stoddart, Consultant, Tessella, Altran World Class Center Analytics, discusses how this data version of the train can be used to model and predict future scenarios and identify optimal outcomes.

One of the most promising aspects of the rail industry’s digital transformation is predictive maintenance – using data collected on equipment during operation to identify maintenance issues in real time. This means repairs can be properly planned, with the benefits that trains don’t need to be unexpectedly taken out of service for emergency or unnecessary routine maintenance.

There is a lot of hype around this topic and several failed cases. The explosion of IoT sensors and platforms – some overpromising what they can achieve – has created a belief that simply plugging in a few devices will give you all the data you need. A number of poorly planned investments have become costly and fallen short of the expected business transformation.

This should sound a note of caution about buying into hype, but it doesn’t mean predictive maintenance is out of reach. The benefits are there, but they need proper planning and management. As with any promised revolution, there is no magic bullet.


Is predictive maintenance a good rail investment?

Fleets of trains last a long time. Planes and cars are renewed more regularly to take advantage of efficiency improvements from new designs. This is less of a driving factor for trains and the strategic focus tends to be on keeping them in service for as long as possible, to get value out of the considerable initial investment.

That means that technologies enabling predictive maintenance, reducing operating costs and extending a fleet’s lifetime, have the potential to deliver huge financial rewards. However, it also means that older trains currently in service, that were not built for modern connectivity, require investment to be able to do so.

Therefore, there is an important cost-benefit case which must be made ahead of any major investment into predictive maintenance.


Collecting the data for predictive maintenance

Our experience in rail and other sectors is that successful analytics projects start with clarifying the problems that need to be solved and the decisions that are needed to advance forward. Only then should the project consider how to identify the necessary data and the best technology to capture and process it. This helps to work out whether such an investment will be worthwhile.

Two examples of rail operators with similar mid-life commuter fleets that illustrate this spring to mind. Firstly, a company who was installing black box data recorders to meet statutory requirements. Their fleet was experiencing reliability and operational problems, so having committed to the mandatory refit, the company took the opportunity to ask what else it could do to collect data that would improve fleet performance and address the particular problems.


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