Case Study: Talgo achieves real-time predictive maintenance with Looker

Talgo Streaming two thousand events a second at three hundred kilometers an hour

Talgo, the Spanish high-speed train manufacturer, needed a way to monitor trains in real time and move from routine maintenance to predictive, condition-based maintenance without reducing service availability. Working with Looker as part of its Google Cloud setup, Talgo also needed better reporting and visibility for maintenance teams across multiple countries.

Looker helped Talgo turn massive sensor and event data into usable dashboards and automated reports, alongside a cloud data architecture for streaming and analysis. The result was real-time monitoring from 2,000 onboard sensors, 2GB of data collected per train per day, 2,000% more data than before, and machine learning insights that could save more than 200 hours of inspection time per train each year.

Talgo

José Antonio Marcos

Chief Maintenance Engineer