Case Study: CAF achieves safer, scalable predictive maintenance for trains with Amazon Web Services

CAF Increases Train Safety with AWS IoT

CAF, a leading Spanish train designer and manufacturer, needed to reduce lifecycle maintenance costs and improve safety by predicting and scheduling repairs from real-time operational data. Its Rail Services unit faced the challenge of securely collecting and analyzing sensor data from trains at scale to detect component issues quickly and reliably.

CAF built the LeadMind predictive-maintenance platform on AWS, using AWS IoT Core to ingest sensor data (about 15 GB/day from 30 trains, with capacity to scale to hundreds), Amazon Kinesis for real-time streaming, and Amazon Redshift for warehousing and BI integration. By relying on managed AWS services, CAF freed engineering time to build better predictive models, reduced capex, simplified IoT management, and accelerated identification of potential issues to maximize train safety.

CAF

Javier de la Cruz

Rail Services Engineering Head Manager