Wind turbines operate in harsh and variable environments, which can lead to wear and tear on their components over time. Traditional methods of turbine maintenance, such as periodic inspections or rule-based systems, can be time-consuming and may not identify all potential faults or issues.
AI and machine learning (ML) can play a key role in fault detection and diagnosis in the wind energy industry. By training an ML model on data from turbine sensors, our solution can detect potential faults and diagnose issues before they become major problems. In addition, ML models can be used to predict remaining useful life (RUL) of turbine components, enabling wind energy companies to schedule maintenance proactively and reduce downtime.
RapidCanvas AI Solutions impacting
Fault Detection and Diagnosis for Wind Turbines
Reduction in maintenance costs
Increase in turbine reliability
Extract and prepare data from turbine sensors
Get in-time and advanced alerts on potential faults and issues
Access dashboards on turbine performance and maintenance
Build predictive models to detect potential faults and diagnose issues
Use optimization techniques to predict remaining useful life (RUL) of turbine components
Get data-driven insights into the impact of faults and issues on turbine performance