Predictive Maintenance for Wind Energy
ML-powered predictive maintenance for wind farms
Project Overview
Designed predictive maintenance platform for wind farm operations following SAP's acquisition of Fedem simulation software. The platform used ML-based failure prediction to enable proactive maintenance scheduling for renewable energy infrastructure.
My Role
Led UX design, working with engineering teams and domain experts to translate complex ML predictions into actionable operational tools for maintenance teams.
Challenge
Wind turbine failures cause significant downtime and lost energy production. Maintenance teams needed to shift from reactive repairs to proactive intervention based on predictive analytics.
Approach
- Worked with wind farm operators and maintenance engineers to understand workflows
- Designed interfaces for ML-based failure prediction visualization
- Created dashboards for maintenance prioritization and scheduling
- Developed alert systems for critical predictions
- Balanced technical complexity with operational usability
Outcome
Proof-of-concept won board approval and moved to productization. Platform enabled proactive maintenance scheduling, reducing unexpected turbine downtime for renewable energy infrastructure.
Key Learnings
- Domain experts need transparency in ML predictions to build trust
- Operational tools must integrate with existing workflows
- Technical sophistication works when information is actionable
- Renewable energy efficiency directly impacts climate outcomes
Domain
Renewable Energy | Predictive Maintenance | Industrial Operations
Technologies
ML/AI | Enterprise Software | Operational Dashboards