SIEMENS – Data-Driven Rail Systems

When was the last time you experienced inconvenient train delays? Do you wish that railway companies could do a better job of predicting when breakdowns will occur?

Whereas in the past, maintenance traditionally consisted of checking rail vehicles at designated operating centers on a regular basis to resolve obvious problems and maintain machines, digital technologies are opening the door to a new level of service.

Siemens’ Allach locomotive plant on the outskirts of Munich is a high-tech hub for train-related data analysis, which has been using special algorithms to predict when potential breakdowns may occur. Since 2014, it has been home to Siemens’ Mobility Data Services Center (MDS) where experts have been working hand-in-hand with the facility’s Rail Service Center to translate complex streams of mobility-related data into optimized operations.

The data streams from locomotives, high-speed trains, and local trains from Europe and other non-European countries converge at the MDS Center. By drawing upon this data, the organization’s 20 programmers, database experts, and implementation managers have developed a data-driven service offering that is unrivaled in the rail sector in terms of real-time train monitoring, forecasting of wear and tear and failure of components, and analysis of complex vehicle problems.

The result: Before a rail vehicle rolls into Siemens’ Service Center, its technicians already know what needs to be done, thus keeping the maximum number of trains available for use.

The Allach locomotive plant on the outskirts of Munich is a high-tech location for train-related data analysis.

Since 2014 it has been home to the Siemens Mobility Data Services Center. There, experts have been working hand-in-hand with the facility’s Rail Service Center to translate complex streams of mobility-related data into optimized operations for customers.

Gerhard Kreß is surrounded by trains – both real and virtual. In front of him, on a monitor, is a schematic drawing of a vehicle. A whiteboard looms behind him in the open-plan office of the Siemens Mobility Data Services Center (MDS) that he heads. The whiteboard is dotted with red and blue formulas and equations that describe what happens during train operations. If Kreß looks out the window, he can see the saw-tooth roofs of the Allach industrial site on the outskirts of Munich where Siemens currently builds its Vectron class locomotives. As of October 2015, this is also where the locomotives can be serviced and maintained – at the Rail Service Center located just three rail car lengths away. Leading to the vehicle workshop are not only two tracks, but two worlds: the virtual and the real.

Digital Transformation

The data streams from locomotives, high-speed trains, and local trains from Europe and other, non-european countries converge at the MDS. By drawing upon this data, the organization’s twenty programmers, database experts, and implementation managers have developed a data-driven service offering in the rail sector that is unrivalled in terms of real-time train monitorings, forecasting of wear and failure of components, and analysis of complex vehicle problems.

Infographic: A Pit Stop for Rail Vehicles

“Before a rail vehicle rolls into our Service Center, we already know what needs to be done,” says Kreß. That provides for maximum availability of trains. “We did not begin building this group until mid-2014,” says Kreß. “It was no small thing, because data analysis experts are in great demand.”

The digital transformation is setting the pace for rail technologies. Whereas maintenance traditionally consisted of checking rail vehicles at operating centers on a regular basis, resolving obvious problems, and maintaining machines, digital technology has opened the door to a new level of service. Remotely or locally collected sensor data, error messages, and log files provide MDS employees with an unprecedented level of detail regarding rail vehicles.

The Siemens Rail Service Center in Allach.

The Siemens Rail Service Center in Allach.

Billions of Data Points

Indeed, digital technologies provide experts with much more than just information on standard variables such as speed, braking behavior, and mileage.  They also provide information regarding, for instance, the behavior of compressors, the weight of connected rail cars, and the status of automatic control processes. What is more, the quality of the rails, gradients, and slopes, as well as weather conditions during operation are registered along with the operating frequency of trains in rail networks. “For the future of the Mobility business, vehicles alone are not the decisive factor,” says Kreß. “For customers, it is about the lifetime costs of vehicles and their efficient use. Success can be achieved only with the help of bundled data from vehicles, the infrastructure, and operations.”

All of this results in a veritable mountain of data. A fleet of 100 rail cars produces about 100 to 200 billion data points every year. And that’s just the beginning. As they analyze this data, Kreß and his team are looking for meaningful patterns.  The resulting knowledge can enable the MDS to, for instance, optimize maintenance processes. With gearbox bearings, for example, which are subject to a high level of wear and tear at high speeds, the MDS can predict problems at least three days in advance. Breakdowns are thus avoided, which increases the availability of trains and saves money.

Velaro E trains operated by RENFE

Advanced data analytic systems from Siemens help to make the high-speed Velaro E trains operated by RENFE reliable.

High Speed and Reliability

Behind this approach is a forecasting model developed by the the MDS team that analyzes mobility systems data.  Data analysts first used conventional machine learning algorithms to evaluate the sensor and infrastructure data from a wide variety of trains. This process requires in-depth knowledge of the underlying relationships between systems. Such information can be obtained from train engineers, train manufacturers, and the employees of the new Rail Service Center in Allach.

Just how well this works can be seen in the high-speed rail line that the Spanish National Railways (Renfe) operate between Madrid and Barcelona. Here, Renfe competes with an air route. The train takes two and a half hours compared to a flight time of an hour and twenty minutes. Renfe, however, guarantees train passengers that they will receive a complete refund of their fare if the train is delayed by 15 minutes or more. To guarantee this high level of reliability, Renfe teamed up with Siemens to establish a joint venture that uses advanced data analysis for trains. The result has been that only one noteworthy delay, related to technical problems, has occurred over the course of around 2,300 trips.

A brake failure that involves error messages, for example, can be normal if the locomotive simultaneously hooks up to a rail car. This kind of knowledge makes it possible to distinguish between important and unimportant factors and recognize causal chains. Thanks to this approach, after just one year Kreß and his team are already able to use forecasting models with a high level of reliability.

  • A Vectron in Norway. The train’s modular construction is a worldwide first.

  • Gerhard Kreß, head of the Siemens Mobility Data Services Center (MDS).

  • Advanced algorithms help Gerhard Kreß and his colleagues predict when potential breakdowns may occur.

  • Thanks to this information, Martina Stöttner, head of the Rail Service Center, and her team can plan repairs while a train is still in service.

  • Once a train reaches the Service Center, maintenance measures are ready for implementation.

  • The result is improved availability for customers.

  • Siemens also builds its Vectron class locomotives in Munich Allach.

  • A Vectron in Norway. The train’s modular construction is a worldwide first.

From Big Data to Focused Solutions

The Mobility Data Services Center offers an additional benefit: Not only can it draw upon datasets from different rail fleets, but from information associated with the different conditions under which those fleets operate – whether in Germany, Spain, or Russia. All of this adds up to an information toolbox that can translate into enhanced rail vehicle reliability. This can be an advantage for smaller operators as well, as they can benefit from MDS services to reduce risks. “Forecasts of breakdowns and wear, error diagnostics, and well-planned maintenance cycles are just the beginning,” says Kreß. “In the future it is conceivable that at the Rail Service Center we will be able to download a vehicle’s complete database, as is now possible with airplanes, in order to review the data for anomalies.”

All in all, thanks to data analytics, MDS provides tremendous added value to its customers. This in turn gives the Siemens Mobility Division a decisive competitive advantage, while for the Siemens Group it represents another step in its journey to becoming a digital company. Yet in the end, this is only possible because Siemens already has extensive knowledge about trains and their maintenance that has been acquired over decades – an expertise that is used on a daily basis at the Rail Service Center in Allach. “As an isolated company, MDS would certainly not be as powerful,” says Kress. “And in the end someone would have to pick up some tools and get to work.”

 

Credit: www.siemens.com