Engineering giant Siemens is using complex data streams gathered throughout Europe and other regions to create a rail monitoring, forecasting and analytical centre of unprecedented capability.

The Rail Service Centre of the Allach locomotive plant is situated on the fringes of Munich, and responsible for performing maintenance and repair work on the vast number of train that arrive at the facility from both Europe and adjacent geographic regions.

In order to further enhance the critical work that the Rail Service Centre performs for cross-regional transit operations, Siemens established the Siemens Mobility Data Services Centre (MDS) at the site in 2014, for the purpose of aggregating vast amounts of mobility and performance-related rail information.

The MDS first gathers complex streams of data on a range of locomotives, including both high-speed trains and local trains, operating in both Europe and countries in adjoining areas.

The centre’s team of 20 programmers, database mavens and operations managers then use this huge slew of information to enhance the maintenance and repair work of the Rail Service Centre by performing sophisticated data analysis on incoming locomotives.

This data analysis includes the monitoring of vehicles in real time, predictions concerning wear and tear and breakdown of components, as well as assessment of complex locomotive issues.

According to the MDS’ managers, the rail data analysis provided by the centre is unprecedented in terms of its capabilities, and serves to optimize real world transit operations.

Thanks to the MDS, engineers are already well-apprised of the work that locomotives require once they arrive at the Rail Service Centre, helping to minimize maintenance time and maximize train availability.

Rail engineers no longer need to perform the traditional, time-consuming tasks of performing regular inspections of vehicles at operating centres. Digital technology enables remote and local sensors to gather data on trains and their performance in real time and during the course of operation.

Digital technologies and sensors enable engineers to compile a vast amount of detailed information on vehicle performance. In addition to the key measures of speed, braking and mileage, they can also assess compressor behaviour, the mass of connected rail cars, as well as information on the surrounding environment, including weather conditions, gradients, slopes and rail quality.

Data analysis even enables the MDS to predict areas of likely breakdown or malfunction so that contingency repair work can be prepared prior to a vehicle’s arrival, greatly enhancing efficiency and availability.

While the benefits of data analysis for rail maintenance and repair operations are obvious, Siemens was unable to begin establishing the MDS until the middle of last year due to surging demand for data analysis experts.

Data analysis could have major implications for the efficiency and optimization of Australia’s own long-haul transit system, given the sheer scale of the country and the size of the rail network conveying both passengers and cargo to distant locations.