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The Union Dividend

Union Pacifi c (UP), the largest rail company in the US, claims that analytics software can predict certain kinds of derailments days or weeks before they are likely to occur, improving safety and potentially saving millions of dollars.

Software located in a control centre analyses data from acoustic and visual sensors on the underside of each carriage. These sensors, in place for over a decade, have already reduced bearing-related derailments by 75%, UP claims.

The control centre can communicate directly with engineers on trains, which can then be taken off the track at the earliest opportunity.

Sensor-based early detection of potential equipment failures, combined with other monitoring capabilities for rail infrastructure (such as track and bridge inspections) can reduce disruptions to passenger and freight service.

Two more points are worth highlighting; continuous (as opposed to discrete) monitoring means that ‘noise’ in the system can be eliminated, or at least measured and compensated for; when measurements are made at fi xed intervals the possibility exists that an important event will be overlooked – for example, a section of track could become contaminated to such an extent that the track circuit momentarily reports a train as not present; this transient signal could be missed by the signalling system but picked up by a continuous monitoring sensor.

Weather data would appear to be a natural extension to the sensor-collected data set; if it was known (forecast) that temperatures in some part of the network were going to soar (or plummet, or there was heavy snow forecast) in, say, three days time then resources could be proactively deployed to that location to attempt to manage the impacts of such a change.

CBM systems increase an operator’s ability to deploy predictive maintenance schemes.

In Britain, Network Rail’s Intelligent Infrastructure initiative is a strategic solution for the whole network that takes such a ‘Predict and Prevent’ approach to maintenance; it’s a key part of the organisation’s overall Asset Management Improvement Plan.

Conclusions About the author

Instrumented and connected devices communicate critical data to software analysis tools that collate and correlate that data,

Mike Mustard is business development manager (Rail) at Findlay Irvine.

exposing trends, relationships and anomalies to inform ‘real time’ decision making.

Systematically collecting that data requires investment in sensor technology, communications networks and analytics software.

Without serious investment in innovation, technology and advanced analysis software, the global rail industry will struggle to meet the increasing demand for freight and passenger transportation.

Today, trackside sensors monitor acoustic signatures, temperature and wheel impact on most North American and many European railways. Wireless networks support the ability to send data from monitored assets in remote locations.

Knowing the location, status and condition of every asset within the entire network implies the creation of an enormous data set, exactly what ‘big data analytics’ is designed to exploit.

When railways were fi rst introduced they represented an industry where cutting edge innovation occurred every day (much like in a modern, young entrepreneurial company). Today it’s much harder to introduce innovations, and the reasons are as much psychological as economic: in mature industries (and large, well-established organisations) an attitude of risk aversion trends to dominate. And, as they mature, companies that were once young, nimble and innovative fi nd it increasingly hard to sustain that early momentum; perhaps, in setting out to transform the rail industry, this will be the greatest challenge of all.

UP is now beginning to use predictive analytics to study noise patterns in the data captured by sensors. The company claims it can evaluate forty million patterns every day and can alert train operators within fi ve minutes of the detection of an anomaly in a bearing. Infrared sensors are placed every 20 miles on its tracks and take about twenty million train wheel temperature readings per day, looking for overheating – a sign of impending failure. There are trackside microphones to listen for growling bearings in the wheels.

Data is sent via fi bre optic cables that run alongside tracks. Analytics software fl ags anomalies, letting experts decide within fi ve minutes of taking a reading whether a driver should pull a train off the track for inspection, or perhaps just slow it down.

Findlay Irvine (FI), formed in Scotland in 1960, is a multi-disciplinary engineering, design and manufacturing company that serves several market sectors, most importantly transport.

The company’s products range from remote monitoring and control systems to data loggers and grip testers; its fi rst product for the rail sector, a carriage heating controller, was developed in the mid-1960s. Since then FI has developed over 300 products, many of which have been custom designed to solve complex engineering problems; the company has earned its reputation as a leading product development organisation, working closely with leading operators and Network Rail in the UK.

Today, FI employs over 20 engineers at its headquarters near Edinburgh.


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rail technology magazine Aug/Sep 13 | 51

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