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Sensory overload

– Why more data may be better than more tracks

Mike Mustard, business development manager at engineering, design and manufacturing company Findlay Irvine, explores the future of railway technology.

IT’s a big problem I

nformation technology has become critical to virtually every aspect of today’s transportation systems. But as demands increase for more capacity, lower costs and increased safety, what tools and techniques should operators adopt to capitalise on the advantages that IT systems can create?

Rather than simply building more tracks or adding more trains, the rail industry of the future will have to collect and exploit a much richer set of data (from a wider range of sources) than it does now, and it will have to learn how to exploit the powerful insights that deep analysis of that data can reveal. The next

Big Brother If

you want to know how seriously

governments and multinational organisations are taking the emergence of big data, consider the following:

• In March 2012, The White House announced a national ‘Big Data Initiative’ that consisted of six Federal departments and agencies committing more than $200m to big data research projects.

• The Massachusetts Institute of Technology (MIT) hosts the Intel Science and Technology Center for Big Data, combining government, corporate, and institutional funding and research efforts.

• The European Commission is funding a two year programme to engage companies, academics and other in discussing Big Data issues. The project aims to defi ne a research and innovation strategy that will guide the European Commission towards the realisation of the ‘Big Data economy’.

• The inaugural professional 2014 Big Data World Championship is to be held in Dallas, Texas.

50 | rail technology magazine Aug/Sep 13

few decades will be defi ned by how operators innovate to take advantage of the huge potential available from data connected by an array of sensors to almost everything.

Network Rail’s 30-year vision for the network is (or can be summarised as) to double capacity whilst halving costs. In their own words, a strategic enabler for this is innovation in the use of technology. It can justifi ably be argued that technology innovation is the key to the safe expansion of capacity and to dramatically reducing costs. But it’s hard to introduce innovations into a mature, physically extensive industry.

The rail network carries more people today than at any time in the last 50 years, but increasing demand is straining existing systems, requiring operators to stress passenger timetables for increased throughput.

Rail freight companies too are experiencing growth and are making signifi cant investments in infrastructure to meet the capacity challenge.

Of course, some argue that the success of the railway depends upon things not changing (the continued existence of mechanical interlockings perhaps providing a good point of departure – as it were); however, it is a truth universally recognised that any organisation consistently avoiding or delaying the introduction of new technology must, sooner or later, lose its competitiveness.

“The arrogance of success is to think that what you did yesterday will be suffi cient for tomorrow.” (Attributed to William Pollard)

Information gathering

Organisations are generating and storing more data to produce better and more detailed performance information on everything from product inventories to sick days. Sophisticated analysis of this data can substantially improve (by better informing) decision making.

Analysing large data sets – so called ‘big data’ – has become a key competitive tool, underpinning new waves of productivity growth and innovation. For example, it allows better segmentation of customers, leading to more tailored products or services.

Big data analytics is the process of examining large amounts of data of various types to uncover hidden patterns, correlations and anomalies.

The goal is simply to help companies make better business decisions; tools that can search, sort, compare and correlate huge volumes of disparate data are enabling insights to be gained that simply weren’t possible using conventional business analysis tools.

It requires a combination of powerful servers, search tools and statistical analysis techniques.

Systematically collecting and analysing such a wide range of data informs both short term resource deployment decisions and longer term policy making, so is critically important to the safe expansion of capacity.

Over time, as the volume of collected data increases, its value increases exponentially.

To enable the collection and transmission of this data from the rail network back to a central server (for analysis) requires an expansion of the deployment of tools (sensors, data loggers, communications networks) across a much wider range of assets.

Some rail operators, for example Union Pacifi c, have been doing this for a while, and are already realising signifi cant benefi ts.

What seems to be the problem?

Mobile condition based monitoring (CBM) systems can provide rail operators with better intelligence through continuous real-time capture and analysis of critical data.

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