Overview
Key Metrics
Wind turbine companies are always looking for ways to improve the reliability and performance of their turbines, as well as reduce the cost of maintaining them over their lifecycle. One key area of focus is the operations and maintenance of the turbines.
By implementing predictive maintenance strategies that use data from the turbines’ sensors to identify potential issues before they occur, wind turbine companies can take proactive steps to prevent unplanned downtime and ensure that their turbines are operating at their best. In addition, implementing processes and procedures to ensure that turbines are properly maintained and repaired when needed can help extend their lifespan and improve their overall performance.
AI and machine learning (ML) can play a key role in optimizing wind turbine operations and maintenance. By training an ML model to analyze data from the turbines’ sensors and identify patterns or anomalies, wind turbine companies can proactively address potential issues before they become major problems. In addition, ML models can be used to optimize the scheduling and routing of maintenance personnel, helping to ensure that they are able to efficiently and effectively address issues as they arise. By leveraging the power of AI and ML, wind turbine companies can improve the reliability and performance of their turbines, as well as reduce the cost of maintaining them.
RapidCanvas AI Solutions impacting
Predictive Maintenance of Wind Turbines
Cost of downtime without AI
per machine, per year
$100,000
Cost of downtime with AI
per machine, per year
~$80,000
Highlights
Extract and prepare data from turbine sensors
Get in-time and advanced alerts on potential defects
Access dashboards on turbine performance and reliability
Build predictive models to forecast maintenance needs based on historical data
Use optimization techniques to align resources, schedule and workload
Get data driven insights into operations and maintenance