AI-driven Predictive Maintenance for Rail Systems

Zühlke acts as a global innovation partner, translating deep engineering roots into measurable impact for the transport sector. We specialize in replacing manual, reactive processes with proactive, automated systems.

Key Project Highlights with SBB:

• Automated Pantograph Inspection: We built a machine learning-based cloud solution that uses industrial cameras to measure the profile of coal inserts. This eliminates the need for service workers to manually climb roofs and reduces time-consuming coordination.

• Predictive Wheel Monitoring: Our team developed a live dashboard and "Health Index" to monitor wheel characteristics and rank trains by their maintenance needs.

• Business Value: These solutions enable precise maintenance planning, spotted problems early, and significantly lowered operational costs.

• Our Approach: We work in agile Scrum teams of data scientists and software engineers to deliver continuous software increments. Our technical stack for rail includes Computer Vision, Deep Learning, and secure cloud architectures.