Using sophisticated sensors and analytics Spain’s high-speed trains achieve 99.98% availability, allowing the rail company to operate without reserve units.
The Bangkok metro also uses advanced analytics to achieve 99.55% availability.
Equipping trains with sensors that monitor wear and tear of individual parts, using advanced analytics to forecast preventive maintenance, and scheduling replacements in advance has helped rail companies increase their performance, increase availability and avoid accidents and delays.
According to Siemens, a leading provider of rail technology and data management, rail vehicles today collect and send over 1 billion data points per year, information that is delivered to the maintenance and analytics center.
Using connected IoT sensors, historic data and advanced analytics, rail operators can turn that data into information they can use to drive appropriate actions. This can be used to ensure almost 100% operation availability of the trains and rail infrastructure.
In order to achieve that goal, rail operators need to combine data they obtain from sensors on the vehicles, the infrastructure, and other sources, such as weather data and passenger ridership numbers, to create analytic models that can predict potential failure, even forecast problems in rare circumstances.
Spain has one of the largest high-speed rail networks in the world, second only to China. Using technology from Siemens, advanced analytics, and preventive maintenance, Renfe, the national rail operator, has 99.98% percent availability of high-speed trains for operation, eliminating the need to use reserve units to provide regular service. That allows Renfe to give customers full refunds if a train arrives at its destination more than 15 minutes late.
IoT and analytics also save a significant amount of time and money on maintenance, eliminating the need to replace parts when they are still in good condition, and scheduling preventive repairs and checks when technicians and parts are available, effectively improving the supply chain and saving time.
Many of those parts can still be used for longer periods, but in some cases they need to be replaced sooner. Extreme weather conditions can cause some parts to wear more rapidly, causing technical failures if not prevented. Using advanced sensors, accumulated service data, and weather modeling, it is possible to forecast with high accuracy when parts are going to fail.
“Many rail operators are still replacing parts on a fixed timetable,” said Gerhard Kreß, Director of Mobility Data Services at Siemens, during a recent webinar hosted by MIT Sloan Management Review. He added: “with IoT [sensors] and data analysis they can schedule repairs and maintenance on demand, when parts really need replacement, and there is availability of components, technicians and garages to perform the job”. This way there is no disruption to availability or service, and rail operators can avoid potential problems in their operations.
It is important that the rail operators start to look into the advanced solutions using IoT and Big Data analytics for their operations. Just upgrading old trains and infrastructure with new sensors and connectivity can provide significant operational savings, and avoid service disruptions.
At a time when most public and private rail services — local, regional and long distance — are struggling to keep costs down and remain profitable, turning to IoT and advanced analytics could be part of the solution.