Kevin Price from Infor looks at the role of big data and analytics in boosting asset management in the rail industry

With recent figures from the Office of Rail and Road showing a record 1.69 billion passenger rail journeys in Great Britain in the 2015–2016 period, up two per cent on 2015 and a staggering 129.8 per cent since 1994-1995, it’s not surprising that according to some, we’re at the start of a new Golden Age of rail travel. This new Golden Age inevitably puts added pressure on those rail companies who are responsible for facilitating this increase in passenger journeys. Rail subsidies may have risen, but spend per journey has decreased, with rail businesses the world over faced with the seemingly ever-present conundrum of how to do more with less. Nowhere is this more evident than when it comes to the efficient and effective management of the rail infrastructure.

Rail businesses are well aware of the fact that continuous investment in the rail infrastructure is key, as are Governments across the globe. The UK’s Network Rail is currently undertaking a £38 billion programme of upgrades to its network, and in the US, under MAP-21 legislation, the US Government have made funding grants totaling $10 billion available over the next ten years for transit agencies to maintain bus and rail systems in a ‘State of Good Repair’. These investments surrounding repairing infrastructure assets and prolonging the infrastructure, as well as keeping them safe to operate on and around are monumental. However, the only way to resolve the gap between funding and growth is to become more efficient and make better decisions about how to invest limited resources. This is where preventative maintenance comes into play.

Why preventative maintenance?
With the right systems in place, rail companies can stop the break-fix cycle, improving the ratio of corrective maintenance to preventative maintenance. By identifying looming faults and forecasting the optimal time for maintenance, the right predictive maintenance solution ultimately allows rail companies to proactively manage their many and varied assets before service is unduly affected.

For those rail businesses with responsibility for infrastructure management, the sheer complexity of the assets involved, including a mixture of linear, point, networked, vertical and componentised assets, means that any solution which provides a better understanding of the state and operating conditions of these key rail assets is key. The more proactively and cohesively these often disparate assets are managed, then the more likely the rail company is to achieve its core objectives of safety, service and efficiency throughout the infrastructure, as well as increasing the potential to maximise every asset’s ability to produce revenue.

As part of a distinct move away from a one-size-fits-all culture of compliance, an effective predictive maintenance system goes a long way to helping rail companies to evaluate where an infrastructure’s greatest vulnerabilities lie. By building up an accurate picture of resources and their reliability, monitoring exactly how assets do and should behave, rail businesses can adjust their maintenance strategies accordingly and implement smarter maintenance planning, boosting reliability, mitigating against risk and optimising costs.

Data is king
Predictive maintenance is only now really coming into its own. This is due, in part, to the new breed of applications out there which have the ability to support every type of asset used in the rail industry, as well as empower a mobile workforce to report faults and anomalies in real-time. As is the case with pretty much all industries, assets are increasingly generating more data. And, with technology in the rail sector evolving to facilitate the shift from analogue to digital monitoring equipment, improving the speed and reliability of data capture, this amount of data will only increase.

For example, there are systems available to manage rail conditions, collecting, viewing, analysing and managing every dimension of a rail company’s infrastructure and its condition over time. This can involve managing track geometry measurements and exception data, as well as longitudinal rail profile measurements and rail defects. The technology is out there to analyse multiple measurements of linear assets such as tracks or overhead lines, determining deterioration rates and creating the ability to predict future conditions or dates for potential asset failure.

With preventative maintenance analytics in place, rail businesses are in a good position to exploit this Big Data, identifying patterns that can help to more accurately predict future performance of assets. Data from on-signal sensors, for example, can be integrated with data from visual inspections, manual measurements, videos and operational data, which is then fed into a central system where it’s presented to end users in a format tailored to their individual needs. What results are faster, more accurate insights which inform and enable a move away from interval-based maintenance towards the more efficient condition-based maintenance.

A bright future
Without a solid, robust, well-maintained infrastructure, there can be no new Golden Age of rail travel. Effective predictive maintenance, combined with analytics and the right processes in place means that rail companies can manage planned and corrective infrastructure maintenance more efficiently, boosting productivity, lessening risk and increasing customer satisfaction, while adhering to strict safety, legislative and ISO5500 and ISO 14224 requirements.

As more and more rail companies embrace the benefits that a modern preventative maintenance programme can bring, unplanned service interruptions, outages due to equipment failures and the resulting customer dissatisfaction will occur much less frequently. As the need for large scale efficiencies throughout the global rail industry shows no sign of abating, businesses could do much worse than invest in predictive maintenance with a view to achieving those all-important efficiency savings while improving service levels and making a healthy profit.

The value of Big Data

  • Determine equipment maintenance schedule using real data
  • Ascertain appropriate depth of analytics in order to expedite value
  • Pinpoint unreliable assets / suppliers / processes
  • Predict reliability issues before they happen
  • Ensure uptime
  • Achieve compliance with Government and industry regulations
  • Prolong asset lifespan