Turbit provides a cloud‑native AI platform that aggregates real‑time SCADA and historical turbine data to detect emerging faults with deep‑learning models. The system automatically generates maintenance work orders and risk scores, delivering dashboards and API integrations that enable wind farm operators and asset managers to plan proactive maintenance and reduce downtime across large fleets.
Funding
Funding not disclosed
Founders
Product
Problem
Wind farm operators and asset managers must manually process large volumes of sensor data, coordinate disparate maintenance tasks, and rely on reactive fault detection, which leads to extended downtime and elevated operational risk. The lack of a unified, intelligent platform hampers timely decision‑making and efficient use of resources.
Solution
Turbit delivers a scalable artificial‑intelligence platform that centralizes turbine data, automates routine administrative workflows, and continuously monitors equipment health to identify emerging faults before they cause failures. Its cloud‑native architecture ingests real‑time SCADA streams and historical asset years of data, applying continuously trained neural networks to generate actionable alerts and risk scores. Operators receive consolidated dashboards and automated work orders, enabling proactive maintenance planning and reduced monitoring effort. By leveraging the rapid advancement of AI models, Turbit’s solution improves over time, delivering higher detection accuracy and operational efficiency across large, geographically dispersed wind portfolios.
Target Audience
The primary customers are wind farm owners, asset management teams, and operations‑and‑maintenance service providers seeking to automate monitoring, reduce downtime, and enhance risk management across large turbine fleets.
Features
- AI‑driven emerging fault detection using deep‑learning models trained on 20k+ asset years of turbine data
- Automated generation and routing of maintenance work orders to reduce manual administrative effort
- Centralized data lake that aggregates real‑time SCADA, sensor logs, and historical performance metrics
- Risk‑management engine that scores asset health and prioritizes interventions to minimize downtime
- Scalable cloud infrastructure supporting monitoring of 3,500+ turbines with on‑demand compute resources
- Interactive web dashboard with customizable alerts, trend visualizations, and KPI tracking for operators
- API integration layer for seamless connection to existing O&M and enterprise asset management systems
- Continuous model retraining pipeline that incorporates new data to improve detection accuracy over time