Case Study: Siemens AG achieves predictive, condition-based maintenance and reduced train failures with Teradata Aster Discovery Platform

Analysing sensor data helps Siemens keep operators on track by reducing train failures

Siemens Mobility faced the challenge of meeting strict uptime and long‑term performance guarantees while moving beyond reactive and routine maintenance. To keep operators on time and manage the financial risk of performance‑based contracts, the company needed to use and re‑use vast amounts of diagnostic sensor data to predict component failures before they disrupt service.

Siemens built an “Internet of Trains,” analysing near‑real‑time sensor streams (using tools such as the Teradata Aster platform) to identify failure patterns and predict faults days in advance. Validated on millions of readings, the approach enabled early inspections that prevented breakdowns (for example, RENFE saw only 1 noticeable delay in 2,300 journeys), increased uptime, reduced labor and maintenance costs, and supported performance‑based service contracts and new revenue models.

Siemens AG

Gerhard Kress

Director of Mobility Data Services