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How Siemens uses data analytics to make trains run on time

Tom Macaulay | Oct. 26, 2017
The engineering company has made railway maintenance proactive by monitoring sensor information for the warning signs of failures

rail train den belitsky
Credit: iStock/den-belitsky

Siemens has engineered trains for almost 150 years, including the first electric passenger locomotive in 1879. However, its more recent innovations on the track are driven by data analytics. Using sensors to analyse information on trains and tracks has helped it move railway maintenance methods from reactive to proactive.

By assessing the condition of components through diagnostic sensor data the company can start to spot patterns that indicate when a failure is likely to arise. Then, by monitoring information in near real-time, Siemens can quickly react to concerns before they disrupt services. If an anomaly is detected, the component is sent for inspection.

The benefits of this approach include a reduction in delays, increased mileage, lower labour costs and more efficient maintenance scheduling. This allows Siemens to start offering clients more performance-based maintenance contracts.

Applying data science to the track

A few years ago in a locomotive factory in Germany, Siemens brought together a team of data scientists and engineers to create algorithms that predict failures of train components and railway infrastructure.

"The reason for this is that industrial data behaves differently to internet data, and a lot of the classical analytical models that we use don't work very well in this environment," Gerhard Kress, director of mobility data services at Siemens explains to Computerworld UK. "Also, because these components don't fail very often, you need extremely high prediction accuracies, much higher than anything else we've seen before."

In the last two years alone, his team has filed 30 different patents on new mathematical approaches.

In 2013, Siemens turned to big data vendor Teradata to develop these models into advanced data analytics capabilities. Siemens deployed its own version of the Teradata Unified Data Architecture (UDA) encompassing a data warehouse, Aster Discovery analytics tools, and an appliance for Hadoop.

The enhanced monitoring capabilities that the predictive analytics brings have pushed the availability of Siemens' high-speed trains in Russia to 99.96 percent and metro locomotives in Thailand to 99.98 percent.

Siemens also uses this framework to provide proactive maintenance for numerous regional trains in the UK, including London's Thameslink railway system.


The sensor setup on a train

Kress breaks his data analytics strategy for trains down into three elements: understanding the condition of the different components to predict failure; supporting the passenger experience through climate, smoothness of the ride and functioning toilets; and maximising energy efficiency to cut running costs.

"The energy consumption of a train over its lifetime costs more than buying the train," he says. "You can easily reduce that by 10 percent if you do it right."

A locomotive typically has 150-200 sensors, and a high-speed train 300-350 sensors per carriage. These could include a couple of sensors in each brake alone, which analyse the brake pressure and hydraulic oils to guarantee the train is braking in time. They measure component temperatures and pressures and compare the data to the thousands of reports of failures and fixes in their records.


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