SILICON Valley buzzwords such as big data, the Internet of Things, and digitisation have taken hold of the transport industry over the last few years. Delegates at conferences, summits, and trade shows as well as suppliers in all sectors of the industry are now grappling to understand these terms and how they can be applied to their business.

CN GublerEuropean railways and operators are particularly keen to harness this new network of information. Rail freight volumes continue to fall as the competitive automotive industry strides towards self-driving freight vehicles to gain a greater foothold in the transport market.

Self-driving vehicles and other smart automotive technology not only utilise but also capitalise on big data. The automotive industry has taken countless bytes and bits of data and turned it into the technology of the future that creates efficient vehicles and transport systems.

How is it that the automotive industry seems to have taken advantage of these concepts and advance quickly, while freight and passenger rail is seemingly lagging? Why isn’t the rail industry harnessing big data to improve railway efficiency and locomotive utilisation?

The data in Table 1 helps shed light on rail versus automotive and how each creates and manages new outputs in the market. While this analysis is not completely fair, as the rail industry has very high costs in infrastructure and maintenance compared with the automotive industry, it does show a stark difference in research and development (R&D) support. At a time when most major industries have started referring to R&D more directly as innovation, this apparent lack of support for future creative rail solutions falls cleanly in line with the industry’s recent performance. Nevertheless, rail funding is moving in the right direction with projects such as Shift2Rail that will strive to bring innovative products to the industry.

North American railways are working to implement complex train-control and energy management solutions, including Positive Train Control, that align with the many ERTMS projects throughout Europe.

A growing similarity between the European and North American markets is privatisation. For large North American Class 1 freight railways and smaller regional operators, this is a foundational structure.

Now in Europe there is a growing shift towards privatisation as well. With the Italian government planning to sell up to 40% of Italian State Railways (FS) and emerging industry competitors such as Leo Express in the Czech Republic challenging the status quo of public operation, demand is on the rise for a more holistic solution that supports automation and innovation.

But the question facing both rail and road is: how can we actually sift through this information provided by countless onboard systems to proactively and positively help customers and stakeholders?

Remote monitoring

For Wi-Tronix, a growing technology supplier based in Chicago, this question is what sparked the beginning of modern day remote monitoring and data harvesting application in the rail industry.

In 2005, we introduced the Wireless Processing Unit (Wi-PU) to the rail industry. This is an asset-agnostic data harvesting system which enables remote downloading and analysis of onboard data, regardless of the asset’s make, model or onboard configuration.

The system interfaces with virtually any electronic device on the locomotive that produces data such as event recorders, on-train monitoring recorders, digital video recorder/CCTV devices, vehicle control systems, and energy management systems.

This hardware is paired with a Software-as-a-Service (SaaS) model that allows railways to benefit from the features and enhancements that their fellow rail operators designed in partnership with Wi-Tronix, which brings a beneficial and profitable source of community innovation to the industry and leverages several competitive advantages for many common operations tactics.

WiTronixtableThe Wi-PU enables rail operators to visualise, access, and analyse big data in a holistic manner by fusing information from multiple systems into one common platform. Today, six of the seven Class 1 railways in North America have installed Wi-PU across their fleet to improve asset utilisation and save fuel.

Canadian National (CN) is using Wi-Tronix solutions to employ telemetric systems that host locomotive operating data and train-handling information.
CN ensures locomotive drivers follow driving instructions by tracking operations via our remote monitoring systems. These provide real-time information about locomotive and train performance by remotely measuring and reporting data to a secure, remote data centre hosted by Wi-Tronix. The system continually monitors train operations to ensure train handling rules are applied and that inactive locomotives are powered down to reduce fuel consumption.

Canada’s national passenger operator, Via Rail installed our telemetry system together with other tools on its locomotives in 2012 which optimised train handling and enabled it to achieve a fuel reduction of more than 3%.

BNSF’s fleet-wide Wi-Tronix deployment gathers data from each locomotive and communicates directly with the BNSF network operations centre in Texas. Use of this technology has supported a BNSF-hosted data centre and server that processes a tremendous amount of information. We are also tied in with BNSF’s Autoscan system, which is used for monitoring crew training locations, and for compiling a locomotive driver’s scorecard, which is largely based on train handling, as well as energy management for compliance and fuel usage.

North American Class 1 railways save an estimated 1600 labour hours annually, which is a 94% productivity improvement per task, by utilising periodic and on-demand downloads for event recorders and diagnosing faults in conjunction with other onboard systems.

Wi-Tronix also supports a predictive maintenance feature called Wi-Nostix which gathers millions of data points and analyses them for anomalies. Wi-Nostix focuses on engine faults and uses proprietary algorithms to predict costly failures and recommend maintenance before damage can be done. This type of planning increases efficiency and asset utilisation and reduces locomotive downtime.

This year we launched our next generation product called Violet, which takes the system integration capabilities of the Wi-PU and combines them into one single-source solution. The product includes an event recorder, digital video recorder/CCTV HD system, crash-hardened memory module and our remote off-boarding functionality to deliver business-critical information in real-time.

This information is particularly valuable for emergency or signals passed at danger investigations and train handling reviews. Operators can use Violet’s externally-mounted camera or most existing HD cameras to combine this functionality with live video feeds to detect things such as track and signal defects, vegetation overgrowth and potential ballast problems.

The importance of this development is that asset utilisation in the traditional sense can now be monitored with one solution that provides energy management, predictive maintenance, and train handling analysis. But it also adds a fourth element: asset-based inspections of infrastructure and operations.

Data produced and tracked by the Federal Railroad Administration (FRA) allows for a more in-depth analysis of this opportunity. By using these kinds of visual analytics to detect just 10% of possible dangerous track defects that lead to derailments, over $US 1.4m in track and asset damage costs can be prevented by large railways with a track network totalling more than 50,000km.

When combined with the continuing maintenance costs for multiple devices onboard and the severe complexity of utilising operational data in the industry, it becomes easier to understand why railways have struggled in recent years to adopt these high-tech solutions. The question now is not if these challenges can be overcome, but when operators worldwide will take full advantage of the global rail supplier base to integrate existing systems and streamline their processes.