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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

The risks of sensor failure means that installing too many could cause more problems than they solve," says Kress. "We try to go to the least number of sensors that we can, simply because the more you put on, the more that can fail."

Motors, gearboxes, bearings and wheels are all mechanically connected and may not all need their own individual sensors. Siemens can instead use a virtual sensor, which calculates errors on each part through algorithms assessing, for example, the rate of heat transfer.

They can also combine the data on different assets so that sensors on the train and the track monitor each other, reducing the quantity of checks that are required.

 

What have been the benefits of analytics?

Siemens previously relied on incident response and routine inspections to keep its trains running. This process would require technicians to open up the train to find the cause of the failure, and then fetch the spare parts and tools before returning to make the repair.

The results had a big impact on repair times and delays. A single broken door on a train could add 10-15 seconds onto the time taken to travel between two stations. After 20 stations, the train could already be five minutes late, pushing the whole route behind schedule for the day. Siemens now monitors the doors on each train and can spot a potential failure before it emerges.

"If there's a problem on a Thameslink door, we can tell you in some cases a week and a half in advance," says Kress.

"A technician can then look at door number five, the right door wing on that carriage, and they go there, check it, put some grease there, and then it goes out again and it does not fail."

Siemens also provides maintenance for Eurostar trains, which traditionally used sensors that would send failure alerts that would forcefully stop the train. These sensors, however, were prone to failures of their own.

"This happened to us a few years ago, when my team didn't exist yet and we had to evacuate 700 people on the track," remembers Kress.

"We had the same thing about a year ago, it looked very similar. We realised, first of all, it was a sensor problem. We believe we figured this out a week and a half before the train would have seen that. We could say to the operator that you need to exchange that sensor on that bearing in that buggy on that side, and they did and there was no disturbance of operation."

 

Why Teradata?

"[Teradata] was the only company in the market that had understood that the world is more than a data warehouse," says Kress. "There was competition for Teradata, but given the structure of our data we needed to have a system that can do more than that, so the UDA for us was the main thing."

 

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